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NSS open-text research brief · 2026 edition

What Health Sciences (Non-Specific) students said in NSS 2026

Teaching Staff is the most frequently mentioned reportable topic in 2026.

01 · The 2026 answer

What changed from 2025?

The 2026 analysis covers 459 classified comments in Health Sciences (Non-Specific).

Teaching Staff is the leading reportable topic in this brief. Its mention rate changed by −5.9 percentage points and its sentiment index by +9.1 from 2025.

Teaching Staff

2025 36.0%
2026 30.1%
Share of classified Health Sciences (Non-Specific) comments mentioning this topic.

02 · Findings

Where sentiment differs from the sector

Topics within this subject are shown only when at least 20 comments support the cut.

Above-sector sentiment

  1. Personal Development

    n=28 · sentiment +61.9 · +1.2 points vs sector · 6.1% mention rate

  2. Teaching Staff

    n=138 · sentiment +55.8 · +13.9 points vs sector · 30.1% mention rate

  3. Student Life

    n=32 · sentiment +47.4 · +4.1 points vs sector · 7.0% mention rate

Below-sector sentiment

  1. Scheduling and Timetabling

    n=27 · sentiment −34.8 · −3.8 points vs sector · 5.9% mention rate

  2. Organisation and Management of Course

    n=64 · sentiment −9.9 · −1.7 points vs sector · 13.9% mention rate

  3. Type and Breadth of Course Content

    n=102 · sentiment +7.8 · −18.1 points vs sector · 22.2% mention rate

03 · Comparisons

Topics compared with the sector

Subject and sector figures are calculated on a like-for-like basis using the same deterministic supervised learning approach.

Topic n Mention rate Sentiment Vs sector
Teaching Staff 138 30.1% +55.8 +13.9
Student Support 117 25.5% +35.0 +0.5
Type and Breadth of Course Content 102 22.2% +7.8 −18.1
Delivery of Teaching 99 21.6% +11.5 −11.4
Placements, Fieldwork and Trips 94 20.5% +7.9 −6.5
Organisation and Management of Course 64 13.9% −9.9 −1.7
Feedback 55 12.0% +0.4 +12.9
Assessment Methods 32 7.0% −12.5 +4.5
Communication with Supervisor, Lecturer, Tutor 32 7.0% +16.0 +11.9
Student Life 32 7.0% +47.4 +4.1

04 · Time series

Current questionnaire period, 2023–2026

The 2023 NSS questionnaire redesign creates a comparability break. We show earlier years separately as context rather than drawing a trend through 2022–2023.

Year Comments
2023 265
2024 555
2025 642
2026 459
Show historical context, 2018–2022

All years were analysed with the same deterministic supervised learning approach, but the survey instrument differs from the current questionnaire.

Year Comments
2018 468
2019 611
2020 848
2021 642
2022 886

05 · Action

Three evidence-linked actions

Use the findings to choose a local test, then check the same topic and cohort again rather than treating a sector pattern as a diagnosis of one provider.

  1. 1

    Stabilise the timetable earlier

    Publish confirmed teaching and assessment patterns as early as possible, coordinate deadlines at programme level and explain unavoidable changes promptly.

    Evidence: Scheduling and Timetabling has a 2026 sentiment index of −34.8 from n=27 comments.

  2. 2

    Make the purpose of assessment explicit

    Map each assessment to its learning purpose, required preparation and place in the programme, then remove avoidable duplication and bunching.

    Evidence: Assessment Methods has a 2026 sentiment index of −12.5 from n=32 comments.

  3. 3

    Give programme operations clear ownership

    Maintain one programme calendar, name owners for recurring decisions and use simple change control when teaching, assessment or staffing plans move.

    Evidence: Organisation and Management of Course has a 2026 sentiment index of −9.9 from n=64 comments.

06 · Method and limits

How to read this evidence

How topics are identified

Deterministic supervised learning models identify topics in each sentence. A comment counts once in every topic it mentions; mention rate is the share of comments included in the analysis for the same population, so topic rates do not sum to 100%.

Sentiment index

The index summarises the balance of positive and negative language from −100 to +100. Scores are averaged within each comment first, so longer comments do not carry more weight.

When results are shown

Pages require at least 100 comments and three reportable topics or subject cuts. Displayed cuts require n≥20; 2026-versus-2025 claims require n≥30 in both years.

Scope

This is authorised aggregate analysis of OfS NSS national undergraduate open-text comments. In 2026, 40,822 of 43,870 source comments were classified (93.1%); mention-rate denominators exclude unclassified comments.

07 · Reuse

Cite this page

Student Voice research team (2026). “Health Sciences (Non-Specific) student feedback: NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/cah3/health-sciences-(non-specific)/

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