4 measurements spanning 11 days, net +1,126. 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”.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
16 Sept 2026, 11:02
1,154
+1,120
8 Sept 2026, 00:36
34
+6
5 Sept 2026, 03:22
28
no change
5 Sept 2026, 02:47
28
first reading
Engagement
12 posts held, back to 8 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 1 page of Telegram’s post history, 20 posts per page.
ERR · 30 days
7.59%
avg views ÷ 1,154 subscribers
Avg views / post
87.6
11 posts measured
Reaction rate
8.22%
reactions ÷ views · ER floor
Posts in window
11
of 12 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. It is computed over the 10 of 11 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 2 September 2026
Posts held
12 (8 August 2026 – 2 September 2026)
Views total
964
Reactions total
77
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
5 Sept 2026, 03:22 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.
Reaction mix
77 reactions across 10 posts, in 1 kind.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
77
100.0%
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 10 of the 12 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 77 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 12 most recent posts we hold, published 8 August 2026 to 2 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.
🩺🤍 پرونده بالینی و تصمیمگیری پرستاری | Case_Study#
دوستای خوبم، قبل از اینکه سراغ متن کامل پوستر برید، یک چالش کوچیک برای خودتون داشته باشید! 🧠✨
بعد از خوندن خلاصه پرونده و شرح حال بیمار، چند دقیقه مکث کنید و سعی کنید خودتون مراحل فرآیند پرستاری این کیس رو بنویسید:
🔹 جمعآوری دادهها و ارزیابی
🔹 تشخیصهای پرستاری
🔹 تعیین اهداف
🔹 برنامهریزی و مداخلات
🔹 ارزیابی نتایج
حالا وقتشه پاسخ خودتون رو با متن پوستر مقایسه …
داروی وارفارین (Warfarin)
🔸️ نام تجاری: کومادین
▫️ دسته دارویی: ضد انعقاد
▪️ شکل دارویی: قرص
🔹️ دوزهای دارویی: ۱، ۲، ۳ و ۵ میلیگرم
مکانیسم: فاکتور های انعقادی ۲، ۷، ۹ و ۱۰ برای ساخته شدن در کبد نیاز به ویتامین K دارن. وارفارین با اختلال در فعالیت ویتامین K باعث مهار ساخت این فاکتور ها میشه
کاربرد: این دارو برای پیشگیری از آمبولی در افراد مبتلا به فیبریلاسیون دهلیزی، پیشگیری از تشکیل لخته پس از عمل دریچه مصنوعی…
🔹️تغیرات برونر2026:
#قسمت_سوم
مقایسه نسخه ۲۰۲۲ و ۲۰۲۶:
۹. مدل سنجش قضاوت بالینی:
یک نکته بسیار مهم:
مدل سنجش قضاوت بالینی (CJMM) در سال ۲۰۲۶ برای نخستین بار وارد کتاب نشده است.
این مدل در نسخه ۲۰۲۲ نیز وجود داشت.
تفاوت اصلی در نسخه جدید، جایگاه آن در ساختار فصل و تأکید بیشتر بر کاربرد آن است.
شش مرحله مدل عبارتاند از:
۱. تشخیص نشانهها
۲. تحلیل نشانهها
۳. اولویتبندی فرضیهها
۴. ایجاد راهحلها
۵. انجام اقدامات
۶…
برونر ۲۰۲۶ چه تغییراتی کرده است؟
#قسمت_دوم
فصل اول: حرفه پرستاری و فعالیت حرفهای پرستار(پارت دوم)
مقایسه نسخه ۲۰۲۲ و ۲۰۲۶:
۴. مراقبت فردمحور:
مفهوم مراقبت بیمارمحور در نسخه ۲۰۲۲ نیز وجود داشت؛ بنابراین نمیتوان آن را یک مطلب کاملاً جدید دانست.
اما نسخه ۲۰۲۶ مفهوم مراقبت فردمحور (Person-Centered Care) را برجستهتر کرده است.
در این دیدگاه، فرد فقط مجموعهای از علائم، بیماریها و تشخیصهای پزشکی نیست. شرایط زندگی، ارز…
#برونر_۲۰۲۶ چه تغییراتی کرده است؟
#قسمت_اول
فصل اول: حرفه پرستاری و فعالیت حرفهای پرستار(پارت اول)
مقایسه نسخه ۲۰۲۲ و ۲۰۲۶:
نسخه جدید کتاب برونر و سودارث فقط چند مطلب تازه به فصل اول اضافه نکرده است؛ بلکه نحوه ارائه و اولویتبندی مطالب نیز تغییر کرده است. در نسخه ۲۰۲۶، برخی مطالب قدیمیتر کوتاه یا ادغام شدهاند و در مقابل، موضوعاتی مانند قضاوت بالینی، آموزش مبتنی بر شایستگی، مراقبت فردمحور، فناوری اطلاعات سلامت و م…
💙 معرفی #اعضای_مرکزی انجمن علمی دانشجویی پرستاری دانشگاه علوم پزشکی رفسنجان | ۱۴۰۵–۱۴۰۶
با انگیزهای تازه برای ساختن، یادگرفتن و اثرگذاری بیشتر 🌱
باهم بیاموزیم و بسازیم.
🆔️@Nursing_RUMS
ممکنه این سوال براتون پیش بیاد که انجمن علمی قراره چه فعالیتهایی داشته باشه؟🤔
از آموزش و نکات بالینی تا تست، پادکست، کیس بالینی و چالشهای هفتگی و...🩵🌱
هدفمون اینه که کنار هم یاد بگیریم، تجربه کنیم و برای پیشبرد پرستاری از نهایت تلاشمون استفاده کنیم🔥
🆔️@Nursing_RUMS
🚀 جای تو توی انجمن خالیه!
اگه اهل ایده، یادگیری، کار تیمی و تجربههای جدیدی، این فرصت رو از دست نده! 💙
به جمع کادر اجرایی انجمن علمی دانشجویی پرستاری رفسنجان بپیوند و باهم بیاموزیم و بسازیم.🌱
جهت عضویت در شورای عمومی و کادر اجرایی به ایدی زیر بخش مورد علاقه خود را ارسال کنید.
🆔️@Nursing_Rums_Admin
📲 برای اطلاع از فعالیتها و برنامهها، عضو کانال انجمن شو:
🆔️@Nursing_RUMS
Showing the 12 most recent of 12 posts we hold for @Nursing_RUMS. 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 1 registered channel — 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.
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 16 September 2026 — this
entry's latest reading, not the date you are reading this.
“🤍 انجمن علمی پرستاری” (@Nursing_RUMS), 1,154 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/Nursing_RUMS.
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.