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Use of clinical placements as a means of recruiting health care professionals to underserviced areas in Southeastern Ontario: Part 2 – Community perspectives

2007· article· en· W2078870157 on OpenAlexafffundabout
Kelly Van Diepen, Michelle MacRae, Margo Paterson

Bibliographic record

VenueAustralian Journal of Rural Health · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsQueen's University
FundersCanadian Medical Association
KeywordsIncentiveAccommodationSample (material)Health careResource (disambiguation)Community healthMedical educationBusinessPublic relationsMedicineNursingPsychologyPolitical sciencePublic healthEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: Part 2 of this two-part study identifies current recruitment strategies and existing incentives used by underserviced communities to recruit health science students during the clinical placement stage. Discussion surrounding current gaps in recruitment strategies and potential funding sources are explored. DESIGN: Mixed-method two-part study using a self-administered questionnaire. SETTINGS: Six community hospitals and one private practice. PARTICIPANTS: Community resource contact from seven underserviced communities in Southeastern Ontario. MAIN OUTCOME MEASURES: Level of community agreement that current recruitment strategies include travel stipends, rent-free accommodation and interprofessional education opportunities. RESULTS: A 100% response rate established that one sample community provides travel stipends, three provide rent-free accommodation, and four offer interprofessional education opportunities. These incentives were frequently offered exclusively to medical students. CONCLUSIONS: When considering the results from part 1 of the study, there is a substantial gap between financial incentives students deem important in the creation of an appealing clinical placement opportunity and the provisions offered to them by the sample communities. The findings of this study support the need for a recruitment enhancement program in Southeastern Ontario.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.309
GPT teacher head0.554
Teacher spread0.245 · 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 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

Citations8
Published2007
Admission routes3
Has abstractyes

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