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Record W4394753818 · doi:10.1136/fmch-2024-002826

Storylines of family medicine IX: people and places—diverse populations and locations of care

2024· article· en· W4394753818 on OpenAlexaff
William Ventres, Leslie A Stone, Emad R Abou-Arab, Julio Meza, David Buck, Jerome W. Crowder, Jennifer Edgoose, Alexander Brown, Ellen Plumb, Amber K Norris, Jay J Allen, Lauren E Giammar, John E Wood, S. Dickson, Gillian Brown

Bibliographic record

VenueFamily Medicine and Community Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsCascades (Canada)
Fundersnot available
KeywordsFamily medicineAlternative medicineFamily memberMedicinePsychologyGerontology

Abstract

fetched live from OpenAlex

is a 12-part series of thematically linked mini-essays with accompanying illustrations that explore the many dimensions of family medicine as interpreted by individual family physicians and medical educators in the USA and elsewhere around the world. In 'IX: people and places-diverse populations and locations of care', authors address the following themes: 'LGBTQIA+health in family medicine', 'A family medicine approach to substance use disorders', 'Shameless medicine for people experiencing homelessness', '''Difficult" encounters-finding the person behind the patient', 'Attending to patients with medically unexplained symptoms', 'Making house calls and home visits', 'Family physicians in the procedure room', 'Robust rural family medicine' and 'Full-spectrum family medicine'. May readers appreciate the breadth of family medicine in these essays.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.226
GPT teacher head0.496
Teacher spread0.269 · 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 designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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