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Record W2412061043

A mixed studies literature review of family physicians' participation in research.

2015· article· en· W2412061043 on OpenAlexaff
Deniz Say Şahin, Mark J. Yaffe⃰, Tamara Sussman, Jane McCusker

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsPsycINFOOperationalizationProtocol (science)ScopusMEDLINEFamily medicineMedicinePsychologyAlternative medicineNursing
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Family physicians' recruitment and adherence in research are challenging. This mixed studies literature review sought to identify the extent of family physicians' participation in primary health care research, as well as facilitators and inhibitors of their recruitment and subsequent protocol adherence in research projects. METHODS: We searched Medline, Embase, PsycINFO, SCOPUS, Google Scholar, and BioMed Central Medical Research Methodology by using an explicit strategy. Sixty-two articles met predetermined selection criteria. Using a mixed method approach, we performed a content analysis of the results published in these articles to synthesize factors affecting family physicians' participation in research. RESULTS: Recruitment rates varied between 2% and 81%. The most frequent types of participation requested were completion of questionnaires (48%) and recruitment of patients (37%). We found that family physicians' personal/professional factors mainly affected recruitment, practice/patient-related issues mainly affected adherence, and study protocol characteristics facilitated both recruitment and adherence of family physicians in research. CONCLUSIONS: This review provides a synthesis of knowledge about factors mediating family physicians' roles in research. Our findings offer material for researchers to create checklists to help create and operationalize protocols that respect local clinical and research realities.

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)0.009
metaresearch head score (Gemma)0.142
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.142
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.704
GPT teacher head0.569
Teacher spread0.135 · 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
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

Citations42
Published2015
Admission routes1
Has abstractyes

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