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Record W4313305089 · doi:10.3233/wor-211301

Family physicians’ sick-listing practices in relation to mental disorders: A descriptive study

2022· article· en· W4313305089 on OpenAlexaffabout
Lauriane Drolet, Pier‐Olivier Caron, Jacques Forget, Jean-Robert Turcotte, Claude Guimond

Bibliographic record

VenueWork · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversité de MontréalUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsSick leaveCertificationDescriptive statisticsFamily medicineMedicineListing (finance)Mental healthDescriptive researchDuration (music)PsychologyPsychiatryNursingPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Mental disorders are among the leading causes of disability for which family physicians are often required to complete sickness certificates. Yet, little is known about family physicians' sick-listing practices in Quebec. OBJECTIVE: This study aims to describe their practices, difficulties and needs. METHODS: Twenty-three family physicians completed a comprehensive questionnaire on sickness certification practices. Descriptive statistics were used. RESULTS: Despite being completed on a weekly basis, sickness certifications were deemed problematic by all participants. While they rarely refused to sick-list a patient, 43.5% reported suggesting accommodations as an alternative to sick leave. Waiting-time to access psychotherapy and delays to set-up workplace accommodations are responsible for many unnecessary sick-leave prolongations. Lack of time, long duration absences, situations where the physician held a different opinion than the patient/healthcare provider and assessing an individual's capacity to work are the most common reported problems. More than half of participants indicated medical schools do not greatly prepare them to carry out these tasks. CONCLUSION: Sickness certifications are deemed problematic, and more training might be key. Our results can be used by medical schools or bodies responsible for continuous education to improve training.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.124
GPT teacher head0.454
Teacher spread0.330 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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