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Record W7098747677

Beyond Retention: National Health Service Corps Participation and Subsequent Practice Locations of a Cohort of Rural Family Physicians

2016· article· en· W7098747677 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)CohortService (business)Rural areaWork (physics)Cohort studyHealth careRural health
DOInot available

Abstract

fetched live from OpenAlex

Background: This report addresses the long-term career paths and retrospective impressions of a cohort of family physicians who served in rural National Health Service Corps (NHSC) sites in return for having received medical school scholarships during the early 1980s. Methods: We surveyed all physicians who graduated from medical school between 1980 and 1983, received NHSC scholarships, completed family medicine residencies, and served in rural areas. Two hundred fifty-eight physicians responded to our survey with complete information, 76 percent of the members of the cohort who could be located and met the study criteria. Results: In 1994 one quarter of the respondents were still practicing in the county to which they had been assigned by the NHSC, an average of 6.1 years after the end of their obligation. Another 27 percent were still in rural practice. Of the entire group, less than 40 percent were in traditional urban private or managed care settings. Conclusions: Although only one quarter of NHSC assignees remain long term in their original assignment counties, they provide a large (and growing) amount of nonobligated service to those areas. Of those who leave, many remain in rural practice or work in community-oriented urban practices. 0 Am

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)0.001
metaresearch head score (Gemma)0.003
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.266
Teacher spread0.235 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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