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Where students go when they are ill: how medical students access health care

2005· article· en· W2068505539 on OpenAlexaboutno aff
Clare Hooper, Richard Meakin, Melvyn Jones

Bibliographic record

VenueMedical Education · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsFamily medicineMedicineReferralQuarter (Canadian coin)Family memberMedical prescriptionHealth careNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.052
GPT teacher head0.529
Teacher spread0.477 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations85
Published2005
Admission routes1
Has abstractyes

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