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Service provision to physicians with mental health and addiction problems

2015· review· en· W276089283 on OpenAlexaboutno aff
María Dolores Braquehais, Andrew Tresidder, Robert L. DuPont

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2015
Typereview
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMental healthMedicineAddictionSick leaveFamily medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Physicians are reluctant to ask for help when they suffer from substance use disorders and/or other mental illnesses (i.e. when they become 'sick doctors'). This can result in greater morbidity/mortality and may lead to significant problems in medical practice. This review aims to describe the nature and development of programs that specifically treat sick doctors [Physician Health Programs (PHPs)]. RECENT FINDINGS: PHPs were first developed in the United States in the late 1970s. The purpose was to identify and treat physicians with problems resulting from mental health issues, mainly substance use disorders. Since then, other PHPs have been developed in Canada, Australia, and the United Kingdom, trying to reach sick doctors, offering counseling or other preventive interventions when needed. New models to help sick doctors, such as the Spanish PHP, were also developed. Counseling and support services for sick doctors have been implemented elsewhere in Europe (e.g. Norway and Switzerland). SUMMARY: PHPs provide interventions specifically designed for physicians and other medical professionals with substance use and other mental health problems. The balance between guaranteeing safe practice and yet encouraging all physicians to ask for help when in trouble raises questions regarding how these programs should be designed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.158
GPT teacher head0.516
Teacher spread0.358 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations38
Published2015
Admission routes1
Has abstractyes

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