What are students actually saying about Career guidance support (NSS 2018–2025)?

Students talk about career guidance in a largely positive way across most cohorts, with some consistent gaps for international, mixed-ethnicity, disabled and apprenticeship learners. Subject differences are material: business, computing and medical areas are warmer; law and HPRS are cooler.

Scope: UK NSS open‑text comments mapped to the Career guidance support category across academic years 2018–2025.
Volume: 9,041 comments (≈2.3% of 385,317 total); 100% sentiment coverage.
Overall mood: 68.8% Positive, 28.8% Negative, 2.3% Neutral; sentiment index +34.7.

What are students saying in this category?

The tone is broadly favourable across mainstream cohorts. Full‑time (+35.2), young (+35.0), male (+35.8) and female (+34.6) groups cluster close to the overall index (+34.7).
Specific groups are less positive and likely need tailored support: Not UK domiciled (+26.1), Mixed ethnicity (+29.9), Apprenticeship (+29.3), and Disabled (+32.0).
Subject patterns matter. Career guidance is rated strongly in Business (+45.2), Computing (+43.0) and Medicine & Dentistry (+38.9). It is notably cooler in Law (+22.3) and Historical/Philosophical/Religious Studies (+16.8), with Social Sciences and Psychology sitting in the low‑30s.
The category draws most of its comments from full‑time (86.9%), young (81.5%), and White (63.8%) students, so smaller cohorts’ signals can be masked unless tracked explicitly.

Segment overview (who’s talking and how they feel)

Share % shows each group’s proportion of comments within this category. Δ vs all compares group sentiment to the overall category index (+34.7).

Group	Share %	Sentiment idx	Δ vs all
Age — Young	81.5	35.0	+0.3
Age — Mature	15.7	34.9	+0.2
Mode — Full-time	86.9	35.2	+0.5
Mode — Part-time	9.8	33.8	−0.9
Mode — Apprenticeship	0.5	29.3	−5.4
Sex — Female	56.5	34.6	−0.1
Sex — Male	40.6	35.8	+1.1
Disability — Not disabled	80.6	35.7	+1.0
Disability — Disabled	16.6	32.0	−2.7
Ethnicity — White	63.8	36.4	+1.7
Ethnicity — Asian	11.8	36.5	+1.8
Ethnicity — Black	3.8	36.0	+1.3
Ethnicity — Mixed	3.1	29.9	−4.8
Ethnicity — Not UK domiciled	9.4	26.1	−8.6

Notes: Unknown/Unspecified groups are excluded from the table but included in overall figures.

Subject split (CAH1) — top 10 by volume

Subject (CAH1)	Share %	n	Sentiment idx
Business and management (CAH17)	10.6	954	45.2
Social sciences (CAH15)	9.0	817	31.2
Subjects allied to medicine (CAH02)	7.7	697	38.3
Computing (CAH11)	5.8	528	43.0
Design, and creative and performing arts (CAH25)	5.5	494	34.0
Engineering and technology (CAH10)	5.1	464	38.0
Biological and sport sciences (CAH03)	4.7	428	32.0
Law (CAH16)	4.5	411	22.3
Psychology (CAH04)	4.5	407	30.2
Medicine and dentistry (CAH01)	2.5	229	38.9

Highlights:

Stronger tone: Business (+45.2), Computing (+43.0), Medicine & Dentistry (+38.9), Subjects allied to medicine (+38.3).
Weaker tone: Law (+22.3) and Historical/Philosophical/Religious Studies (+16.8; n=213). Education & Teaching is very positive (+46.3) but smaller (n=141).

What this means in practice

Ensure equitable access and follow‑through for smaller or less‑served cohorts
- Guarantee evening/lunchtime/digital appointments; offer bookable callbacks in 2–3 working days.
- Track first‑contact-to-resolution for Disabled, Part‑time and Apprenticeship learners; publish a simple SLA.
Strengthen support for international students
- Provide visa/work‑rights briefings, local labour‑market insight and CV/cover‑letter norms by country.
- Use alumni/industry mentors with similar backgrounds; signpost employer sponsorship realities early.
Embed subject‑specific guidance where tone is cooler
- For Law and HPRS, timetable programme‑integrated career tasks (application workshops, mock interviews, employer panels) and map them to assessment calendars.
- Co‑own a minimal careers curriculum with programme leads; monitor attendance and conversion to opportunities.
Make outcomes and pathways visible
- Show “what good looks like” with annotated CVs/portfolios by discipline; publish internship/placement conversion rates.
- Close the loop: “you said / we did / what changed” updates each term.
Operate to a simple quality standard
- One front door for advice; triage and case‑notes; personalised next steps sent within 48–72 hours.
- Dashboards by cohort/subject showing volumes, wait times, and sentiment index changes.

How Student Voice Analytics helps you

Track topic volume and sentiment over time for Career guidance support, with drill‑downs from provider to school/department and cohort.
Compare like‑for‑like across CAH codes and demographics (age, domicile, mode, campus/site); spotlight groups below the overall tone.
Create concise, anonymised briefings for programme teams and careers services; export tables and charts for quick sharing.

FAQs

How is the “sentiment index” calculated?
It’s 100 × (Positive share − Negative share), averaged at category level. Range: −100 to +100.
What does “Share %” mean in the tables?
It’s the proportion of comments within this category attributable to that group (not a sector comparator).