Health & wellness — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 69% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
30 measurements spanning 34 days, net -61. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 15,291–15,382 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 30
Measured (UTC)
Subscribers
Change
8 Sept 2026, 23:18
15,301
-8
5 Sept 2026, 13:00
15,309
-10
3 Sept 2026, 12:57
15,319
+9
2 Sept 2026, 03:24
15,310
+5
1 Sept 2026, 03:27
15,305
-4
31 Aug 2026, 03:59
15,309
-3
30 Aug 2026, 03:05
15,312
-1
29 Aug 2026, 06:35
15,313
-3
28 Aug 2026, 08:12
15,316
-4
27 Aug 2026, 09:54
15,320
+6
26 Aug 2026, 09:26
15,314
-2
25 Aug 2026, 10:36
15,316
-1
24 Aug 2026, 08:27
15,317
-2
22 Aug 2026, 17:47
15,319
-5
21 Aug 2026, 09:36
15,324
-5
20 Aug 2026, 11:48
15,329
-2
19 Aug 2026, 10:38
15,331
-3
17 Aug 2026, 10:47
15,334
-2
15 Aug 2026, 20:52
15,336
-10
14 Aug 2026, 14:08
15,346
first reading
Engagement
33 posts held, back to 3 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 53 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
8.30%
avg views ÷ 15,301 subscribers
Avg views / post
1,270
21 posts measured
Reaction rate
3.51%
reactions ÷ views · ER floor
Posts in window
21
of 33 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 3 September 2026
Posts held
33 (3 August 2026 – 3 September 2026)
Views total
26,654
Reactions total
936
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 10:50 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
1m 24s
Average length
42s
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
1,638 reactions across 33 posts, in 23 distinct kinds. The most used accounts for 51.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
849
51.8%
🔥
208
12.7%
😁
170
10.4%
🤣
84
5.13%
❤🔥
82
5.01%
💔
61
3.72%
🎉
44
2.69%
👍
38
2.32%
🥰
22
1.34%
🤝
21
1.28%
👏
12
0.733%
😭
11
0.672%
💯
7
0.427%
🕊
6
0.366%
😢
5
0.305%
😍
4
0.244%
✍
3
0.183%
😱
3
0.183%
🤩
3
0.183%
🤔
2
0.122%
3 further kinds
3
0.183%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 33 of the 33 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1,638 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 33 most recent posts we hold, published 3 August 2026 to 3 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
1
across the posts below
Posts paid on
1
of 33 we hold a reading for · 3%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @clinic_neformat. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 33 most recent posts we hold for this entry, published 3 August 2026 to 3 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
😱 Открываете ноутбук, чтобы закончить отчёт, но через час вместо этого знаете всё о жизни создателя шоколадных батончиков?
😱 Заходите в супермаркет за молоком, а выходите с тремя видами сыра, новым скрабом — но без молока?
😱 Решаете «быстренько» проверить уведомления и через полчаса уже смотрите видос о том, как чистят аквариум у пираньи?
Если подобное случается с вами постоянно — вы наверняка пробовали «брать себ…
🐸 Цикл лекций о зависти от психиатра Полины Козак.
Стартуем сегодня!
🚩2 сентября — Что такое зависть? (и как понять, что я завидую)
Зависть — сложное чувство, которое может не только мешать, но и быть полезной. Разберём, как распознать её через телесные сигналы, мысли и поведение.
🚩9 сентября — Типы зависти
«Белая» зависть вдохновляет, «чёрная» — разрушает. Узнаем, какие ещё виды существуют и почему важно их различ…
🙌 По нашей доброй осенней традиции мы вместе с проектом Neformat @clinic_neformat продолжаем делиться материалами о травле и личных границах.
🗣 Мы привыкли обращать внимание только на школьную травлю и кибербуллинг, где и агрессорами, и жертвами становятся дети и подростки. Но у травли много лиц.
Сегодня поговорим об обратной стороне школьной реальности — травле молодых учителей. Мы думаем, что эта проблема тоже тр…
😱🌟 Наконец-то осень! Скорей бы листьями пошуршать. А пока вспоминаем, чем нам запомнился август:
⬛️Может, у меня всё-таки БАР? — сравниваем биполярное расстройство и классическую униполярную депрессию.
⬛️«Главное — никому не мешать»: разбираем привычку быть «удобным».
⬛️Почему нас накрывают кризисы и при чём здесь лесные пожары.
⬛️Большая личная история о выгорании: часть 1 — о том, как всё сломалось, и часть 2 — ка…
А если тема зацепила и хочется разобраться глубже — как раз про это у нас будут офлайн лекции в октябре🔥
🗓 Питер 23-го, Москва 27-го.
Мы будем говорить о природе формирования зависимостей, о том, как это проявляется в отношениях и жизненных сценариях.
😀Сегодня до конца дня еще можно купить билеты по раннему бронированию — по минимальной цене.
Подробности по ссылке ⬅️Успевайте!
С нетерпением ждем встречи с вами, …
Почему один человек может попробовать что-то и остановиться, а другой быстро попадает в зависимость?
Ученые давно заметили, что дело не только в среде и триггерах, но и в личностном «фундаменте».
Мы запустили на сайте новый скрининговый тест, построенный на методике SURPS (Substance Use Risk Profile Scale). Это методика, проверенная на выборках в десятке стран и созданная канадской командой, которая изучает зависим…
😧 Ух, завтра в школу? Как вы, Неформатные родители, морально готовы?
К 1 сентября проект Neformat и фонд «Вера» подготовили для вас важный материал.
Что делать, если ребенок боится плохих оценок и сдается при первых сложностях? Наш клинический психолог Милена Вещикова разобрала, как с помощью правильных слов и реакций сформировать у ребенка (и у себя!) устойчивость к неудачам.
🌸 А чтобы начать этот учебный год с по-…
🙂 Вы когда-нибудь задумывались, почему глобальные проблемы мы часто переносим стойко, а потом ломаемся из-за какой-то мелочи?
Чтобы понять, как работает человеческая психика и почему нас накрывают кризисы, наш врач-психиатр, психотерапевт Владимир Черняев предлагает посмотреть на... лесные пожары.
#Владимир_Черняев_nfrmt #neformat_отношения #neformat_концепции
😍 Консультации / Лекторий / Книги
Дорогие наши, крутейшие новости: скоро увидимся с вами вживую — мы снова выдвигаемся в тур🎸
В конце октября отец-основатель Неформата Кирилл Сычёв и наши прекрасные врачи-психиатры, психотерапевты, спикеры Неформатного лектория — Полина Козак и Полина Солнцева — выступят с лекциями о зависимостях в Москве и Питере🔥
Два вечера будем разбираться, как устроены зависимости, что с ними делать и как помогать близким, не …
Это вторая часть истории нашего психолога Елизаветы Коротаевской про выгорание. Если пропустили начало, оно [вот тут] — о том, как любимая работа может незаметно превратиться в истощение и апатию.
А в сегодняшнем посте мы переходим к действиям. Лиза делится правилами и практиками, которые помогли ей восстановиться.
🔗Проверьте, где сейчас находитесь вы: оцените свой уровень выгорания по тесту BAT на нашем сайте. И со…
❤🔥24❤18🔥6🕊3👍1
Showing the 12 most recent of 33 posts we hold for @clinic_neformat. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 8 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Доктор Сычев @dr_sychev · 49,069 Telegram ranks this channel #1 of 94 here — alongside 93 others — read 26 August 2026
Доктор Галеева @doctor_galeeva · 28,550 Telegram ranks this channel #43 of 89 here — alongside 88 others — read 9 September 2026
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 September 2026 — this
entry's latest reading, not the date you are reading this.
“Неформатная клиника” (@clinic_neformat), 15,301 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/clinic_neformat.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.