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Record W2148005420

Physician do not heal thyself. Survey of personal health practices among medical residents.

2003· article· en· W2148005420 on OpenAlexaff
Suzanne Campbell, Dianne Delva

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedical prescriptionConfidentialityMedicineFamily medicineMental healthHealth careNursingPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess how many residents follow the recommendation that physicians have a personal family physician and where residents seek medical attention when needed. DESIGN: Hand-delivered survey. SETTING Residency training programs at Queen's University. PARTICIPANTS: Of 215 residents with a central mailbox, 122 responded (response rate 57%). MAIN OUTCOME MEASURES: Health status, usual access to health care, having a personal family physician, and response to two scenarios. RESULTS: More than a third (38%) of residents have a local family physician, yet 25% of those with chronic illnesses and 40% of those who use prescription medications regularly do not. Many rely on colleagues; 41% have received prescriptions from or written prescriptions for their colleagues. Residents with local family physicians are more likely to seek appropriate medical attention for physical problems. Residents do not recognize or seek treatment for mental health problems. Knowledge, time, and accessibility were considered barriers to adequate health care. CONCLUSION: Many residents do not have good access to comprehensive, confidential, and objective medical care. They rely on colleagues, and they ignore mental health problems. Lack of time and access, and attitudes about the importance of having a family physician are important barriers.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.154
GPT teacher head0.452
Teacher spread0.298 · 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 designObservational
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

Citations55
Published2003
Admission routes1
Has abstractyes

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