Education — 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 22 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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
7 measurements spanning 42 days, net -6. 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 336–344 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Sept 2026, 23:41
337
-3
9 Sept 2026, 22:15
340
+1
30 Aug 2026, 14:04
339
-1
23 Aug 2026, 06:33
340
-1
15 Aug 2026, 17:48
341
-2
7 Aug 2026, 14:32
343
no change
7 Aug 2026, 04:17
343
first reading
Engagement
20 posts held, back to 22 May 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 19 February 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
172 reactions across 19 posts, in 5 distinct kinds. The most used accounts for 90.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
156
90.7%
🤓
6
3.49%
👏
5
2.91%
👍
4
2.33%
🗿
1
0.581%
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 19 of the 20 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 172 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 22 May 2024 to 19 February 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.
فرضاً که شما مدرس یا مشاور آزمونهای تخصصی پزشکی (رزیدنتی، پره یا علوم پایه) هستید، برای ماندگاری در ذهن داوطلبی که تحت فشار درس و کشیکه، باید از «مثلث طلایی محتوا» بهره ببرید.
🔺بدون رعایت توازن در این مثلث، پیج یا کانال شما، یا به یک جزوهدانِ خشک تبدیل میشه که کسی اون رو دنبال نمیکنه، یا به یک صفحه سرگرمی که کسی برای آموزش به اون اعتماد نمیکنه. در بیزنس آموزش پزشکی، اعتبار شما در گروِ مهندسیِ دقیق این سه ضلعه:
…
🔶 راهنمای جامع و گامبهگام استخدام ادمین
پیوست «اهرم نیروی انسانی» در آموزش پزشکی [تدریس و مشاور آزمونهای پزشکی (رزیدنتی، پره، علوم پایه)]
▪️ دپارتمان آموزش کسبوکار
📍 @DMA_channel
فرضاً که شما به عنوان یک مدرس یا مشاور آزمونهای تخصصی پزشکی (رزیدنتی، پره، علوم پایه)، به سقف درآمدی و زمانیِ خودتون رسیدید؛
🔻اینجاست که باید از «اهرمِ نیروی انسانی» برای تکثیر هوش و نفوذتون در بازار استفاده کنید.
🔺بدونِ این اهرم، بیزنس شما به حضور فیزیکیتون وابسته میمونه و عملاً خویشفرما هستید تا صاحب کسبوکار. برای اینکه برند شما از یک نفر به یک دپارتمان تبدیل بشه، باید این ۳ لایهی استراتژیک رو پیادهسازی کن…
فرضاً که شما مدرس یا مشاور کنکوری هستید، ...
فکر خوبیه که در برندینگ شخصی خودتون از «هندسهی آرکتایپها» بهره ببرید.
🔺آرکتایپ (Archetype) یعنی انتخاب یک الگوی شخصیتی ثابت برای نفوذ در ناخودآگاه داوطلب. بدون این هویت، برند شما ممکنه چندپاره به نظر برسه و مخاطب نتونه با شما پیوند عاطفی برقرار کنه.
🔻در بیزنس کنکور، ۳ الگو وجود داره که هر کدوم وزنِ برند شما رو در بازار تغییر میده:
▫️ آرکتایپ حکیم/دانشمند:
~ فلسفه: م…
فرضاً که شما مدرس یا مشاور کنکوری هستید، ...
فکر خوبیه که در کار خودتون از "نیچمارکتینگ" بهره ببرید.
🔸نیچمارکتینگ یعنی تثبیت جایگاه یا تصاحب سهم خودتون از بازار بر اساس یک ویژگی خاص.
برعکس مشاوری که ادعا میکنه «من به تمام داوطلبان کنکور مشاوره میدم»، و ناچار به رقابت در اقیانوس قرمز رقابت قیمتیه.
🔹تئوری تخصص: همونطور که دستمزد یک جراح مغز و اعصاب بسیار بالاتر از یک پزشک عمومیه، در آموزش هم تمرکز بر یک حوزهی م…
دانش و سواد مالی زمانی شاید یک مهارت انتخابی و تجملی بود، اما امروزه به یک ضرورتِ حیاتی تبدیل شده.
اینکه بدونی چطور درآمدزایی، سرمایهگذاری و مدیریت هزینه کنی، علاوه بر سایر جنبههای زندگی، مستقیماً کیفیت مسیر پزشکی—از دوره عمومی و رزیدنتی تا فلوشیپ و فوق و پرکتیس—رو دگرگون میکنه؛ دغدغهها رو کاهش داده و آسودگیِ خاطر رو جایگزین میکنه.
اهمیت این موضوع چیزی از دانش پزشکی یا پژوهش کمتر نیست، حتی بستری رو ایجاد میکنه …
⭐ در «دوره منتورینگ جمعبندی یکماهه پره اسفند ۴۰۴»، منتورها و برنامهریزان آکادمی پزشکی عمیق با اطلاعاتی که از داوطلب دریافت میکنن، مثل پایه درسی فعلی و اهداف و انتظارات از آزمون، نقشهراه شخصی موفقیت داوطلب رو طراحی میکنن.
▫️ دپارتمان آموزش پزشکی
📍@DMA_channel
#آپدیت
دوره منتورینگ جمعبندی یکماهه پره اسفند ۴۰۴
⬅️ منتور: آقای دکتر محمدرضا علینژاد، اینترن دانشگاه ع پ شهیدبهشتی، ۲.۵٪ کشوری پره شهریور ۴۰۴
⬅️ اهداف کلی دوره:
- افزایش بازدهی مطالعاتی
- جلوگیری از تکرار اشتباهات رایج
- کاهش آزمونوخطا و اتلاف وقت
- کاهش استرس و بار روانی
- حفظ پایداری تا روز آزمون
⬅️ همه با شخصیسازی مسیر موفقیت جهت کسب بالاترین نمره ممکن در زمان باقیمانده
⏰️ ظرفیت: فقط ۵ نفر
لطفاً جهت ک…
دوره منتورینگ جمعبندی یکماهه پره اسفند ۴۰۴
⬅️ منتور: خانم دکتر مهرا فکری، اینترن دانشگاه ع پ شهیدبهشتی، رتبه ۲ کشوری پره شهریور ۴۰۴ با نمره ۱۷۸
⬅️ اهداف کلی دوره:
- افزایش بازدهی مطالعاتی
- جلوگیری از تکرار اشتباهات رایج
- کاهش آزمونوخطا و اتلاف وقت
- کاهش استرس و بار روانی
- حفظ پایداری تا روز آزمون
⬅️ همه با شخصیسازی مسیر موفقیت جهت کسب بالاترین نمره ممکن در زمان باقیمانده
⏰️ ظرفیت: فقط ۵ نفر
لطفاً جهت کس…
❤7🤓1
Showing the 12 most recent of 20 posts we hold for @DMA_channel. 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.
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 2 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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@DMA_channel named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@chat_with_dma named in 2 posts, 8 August 2026 – 8 August 2026
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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“Deep Medicine Academy” (@DMA_channel), 337 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/DMA_channel.
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.