MétaCan
Menu
Back to cohort
Record W2240225970 · doi:10.1177/2050312115613352

Modeling factors explaining physicians’ satisfaction with competence

2015· article· en· W2240225970 on OpenAlexaffabout
Rein Lepnurm, Juan Nicolás Peña-Sánchez, Robert Nesdole

Bibliographic record

VenueSAGE Open Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsQueen's UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsCompetence (human resources)MedicineCoping (psychology)Job satisfactionPatient satisfactionHealth careDistressNursingMultilevel modelClinical psychologyFamily medicinePsychologySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Attention to physician wellness has increased as medical practice gains in complexity. Physician satisfaction with practice is critical for quality of care and practice growth. The purpose of this study was to model physicians' self-reported Satisfaction with Competence as a function of their perceptions of the Quality of Health Services, Distress, Coping, Practice Management, Personal Satisfaction and Professional Equity. METHODS: Comprehensive questionnaires were sent to a stratified sample of 5300 physicians across Canada. This cross-sectional study focused on physicians who examined and treated individual patients for a final study population of 2639 physicians. Response bias was negligible. The questionnaires contained measures of Satisfaction with Competence, Quality of Health Services, Distress, Coping, Personal Satisfaction, Practice Management and Professional Equity. Exploring relationships was done using Pearson correlations and one-way analysis of variance. Modeling was by hierarchical regressions. RESULTS: The measures were reliable: Satisfaction with Competence (α = .86), Quality (α = .86), Access (α = .82), Distress (α = .82), Coping (α = .76), Personal Satisfaction (α = .78), Practice Management (α = .89) and the dimensions of Professional Equity (Fulfillment, α = .81; Financial, α = .93; and Recognition, α = .75) with comparative validity. Satisfaction with Competence was positively correlated with Quality (r = .32), Efficiency (r = .37) and Access (r = .32); negatively correlated with Distress (r = -.54); and positively correlated with Coping strategies (r = .43), Personal Satisfaction (r = .57), Practice Management (r = .17), Fulfillment (r = .53), Financial (r = .36) and Recognition (r = .54). Physicians' perceptions on Quality, Efficiency, Access, Distress, Coping, Personal Satisfaction, Practice Management, Fulfillment, Pay and Recognition explained 60.2% of the variation in Satisfaction with Competence, controlling for years in practice, self-reported health and duties of physicians. CONCLUSION: Satisfaction with Competence could be affected by excessive accumulation of duties, concerns about quality, efficiency, access, excessive distress, inadequate coping abilities, personal satisfaction with life as a physician, challenges in managing practices and persistent inequities among physicians.

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)0.003
metaresearch head score (Gemma)0.020
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.243
GPT teacher head0.464
Teacher spread0.221 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueSAGE Open MedicineSame topicPatient Satisfaction in HealthcareFrench-language works237,207