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Record W3163963308 · doi:10.3122/jabfm.2021.03.200436

Clinician Use of Primary Care Research Reports

2021· article· en· W3163963308 on OpenAlexaff
William R. Phillips, Elizabeth Sturgiss, Angela Wei Hong Yang, Paul Glasziou, Tim olde Hartman, Aaron Orkin, Grant Russell, Chris van Weel

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeneralizability theoryMedicineContext (archaeology)Primary carePsychological interventionMedical educationMEDLINENursingFamily medicinePsychology

Abstract

fetched live from OpenAlex

PURPOSE: To assess how primary care practitioners use reports of general health care (GHC) and primary care (PC) research and how well reports deliver what they need to inform clinical practice. METHODS: International, interprofessional online survey, 2019, of primary care clinicians who see patients at least half time. Respondents used frequency scales to report how often they access both GHC and PC research and how frequently reports meet needs. Free-text short comments recorded comments and suggestions. RESULTS: Survey yielded 252 respondents across 29 nations, 55% (121) women, including 88% (195) physicians, nurses 5% (11), and physician assistants 3% (7). Practitioners read research reports frequently but find they usually fail to meet their needs. For PC research, 33% (77) accessed original reports in academic journals weekly or daily, and 36% found reports meet needs "frequently" or "always." They access reports of GHC research slightly more often but find them somewhat less useful. CONCLUSIONS: PC practitioners access original research in academic journals frequently but find reports meet information needs less than half the time. PC research reflects the unique PC setting and so reporting has distinct focus, needs, and challenges. Practitioners desire improved reporting of study context, interventions, relationships, generalizability, and implementation.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.329
GPT teacher head0.533
Teacher spread0.204 · 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.

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

Citations12
Published2021
Admission routes1
Has abstractyes

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