Where students go when they are ill: how medical students access health care
Bibliographic record
Abstract
BACKGROUND: Doctors have high levels of self-treatment, investigation and referral, but little is known about how medical students seek health care. Methods We carried out a questionnaire survey of Year 2 and 4 students, exploring their health-seeking behaviour and attitudes to self-care. SETTING: A London medical school. RESULTS: The response rate was 80%. Nearly all students (99%) were registered with a general practitioner (GP). A total of 43% had informally consulted doctors who were friends or relatives in the previous 12 months (61% of those with a doctor as a family member had informally consulted, and 33% of those without a doctor as a family member had informally consulted; P = 0.001). In all, 13% of Year 4 students and 2.2% of Year 2 students had received a prescription from a friend (P = 0.007). Almost a quarter (22%) of Year 4 and 1.3% of Year 2 students reported having directly contacted a specialist (P = 0.01). A third (32%) (43% Year 4, 1.3% Year 2; P = 0.006) of those referred in the previous 12 months had contacted the consultant directly. In all, 9.2% (0% Year 2, 20% Year 4; P = 0.001) had initiated their own investigations, and 25% (47% Year 4, 7% Year 2; P = 0.001) had been examined by a colleague. Students agreed that it was appropriate for doctors to self-investigate (52%), self-refer (59.1%) and self-prescribe (39.2%). CONCLUSION: Medical students appear to bypass their GPs and initiate investigations, referrals or treatment. This is associated with increased clinical access or access through family members. Self-management of illness is learnt early on in students' careers and is increased with availability and increasing clinical access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".