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Record W2736104305

Family Medicine Research in the United States From the late 1960s Into the Future.

2017· article· en· W2736104305 on OpenAlexaff
Marjorie A. Bowman, Sean C. Lucan, Thomas C. Rosenthal, Arch G. Mainous, Paul A. James

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsSpecialtyFamily medicineAlternative medicineMedical educationMedicineField (mathematics)Public relationsPolitical sciencePathology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: When the new field of family medicine research began a half century ago, multiple individuals and organizations emphasized that research was a key mission. Since the field's inception, there have been notable research successes for which family medicine organizations, researchers, and leaders-assisted by federal and state governments and private foundations-can take credit. Research is a requirement for family medicine residency programs but not individual residents, and multiple family medicine departments offer research training in various forms for learners at all levels, including research fellowships. Family physicians have developed practice-based research networks (PBRNs) to conduct investigations and generate new knowledge. The field of family medicine has seen the creation of new journals to support the publication of research relevant to practicing family physicians. Nonetheless, in spite of much growth and many successes, family physicians and their research have been underrepresented in research funding. Clinical presentations in family medicine are often complex, poorly-differentiated, and exist as one of several patient complaints and diagnoses, and are not well-covered by the narrow basic-science and specialty research that defines most of the biomedical research enterprise. Overall health in the United States would benefit from a more robust research participation and greater support for family medicine research.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.378
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.256
GPT teacher head0.492
Teacher spread0.236 · 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 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

Citations38
Published2017
Admission routes1
Has abstractyes

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