MétaCan
Menu
Back to cohort
Record W7657576

Impact of an interprofessional rural health care practice education experience on students and communities.

2008· article· en· W7657576 on OpenAlexaffabout
Grant Charles, Lesley Bainbridge, Kathy Copeman-Stewart, Rosemin Kassam, Shelly Tiffin

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Interprofessional educationNursingMedical educationHealth careProgram evaluationWork (physics)Human servicesService (business)Rural healthRural areaMedicinePsychologyPolitical scienceBusiness
DOInot available

Abstract

fetched live from OpenAlex

The Interprofessional Rural Program of British Columbia (IRPBC) was established in 2003 as a pilot program aimed at supporting the recruitment of health and human service professionals to rural communities in British Columbia, Canada. The program was designed to expose students in the health and human service professions to rural communities and to assess whether this exposure increased the likelihood of their return to work in nonurban settings once they completed their studies. The initial qualitative evaluation of IRPBC was conducted via individual interviews in the first year and written questionnaires in the second year. In general, IRPBC was perceived by the participants to have had a significant impact on the students and communities. The students who participated in it benefited not only from the chance to engage in rural practice but also from the opportunity to interact within an interprofessional context; and the communities participating in the program profited from enhanced health care and the possibility of attracting new practitioners from these students. Exposure to new ways of providing service and the impact that the introduction of teams of passionate students can have on both practitioners and small communities have greatly enriched the broader communities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.083
GPT teacher head0.521
Teacher spread0.438 · 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

Citations22
Published2008
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicGlobal Health Workforce IssuesFrench-language works237,207