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Record W4413101955 · doi:10.4103/cjrm.cjrm_58_24

A rolodex of skills and roles: listening and learning from northern physician recruiters

2025· article· en· W4413101955 on OpenAlexaffvenueabout
Cheri Bethune, Holly Fleming, Eloho Ukochovwera Ologan, Ghislaine Attema, Erin Cameron

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsLakehead UniversityLaurentian UniversityNOSM University
Fundersnot available
KeywordsActive listeningPsychologyMedical educationMedicineCommunication

Abstract

fetched live from OpenAlex

INTRODUCTION: In Canada, 18% of the population lives in rural areas, yet only 8% of practising physicians work rurally. Rural communities face ongoing challenges in both recruitment and retention of physicians and other healthcare professionals. Improving quality and access to care for rural Canadians rests on the successful recruitment and retention of community-based physicians. The aim of this study was a broad exploration of the roles, experiences, and strategies of physician recruiters. Their stories, when woven together, provide us with valuable insights from the "front lines." METHODS: Using a grounded theory approach, we conducted semi-structured interviews with Northern Ontario Physician Recruiters. RESULTS: Twelve physician recruiters were interviewed. Six themes were identified: (1) Community engagement: Big and small 'e' engagement, (2) Recruiter role: Know thyself, know thy community, (3) Strategies: It is all about movement: reaching people, pulling strings and dodging bullets, (4) Outcomes: listening, learning, liaising, (5) LEARN (er) ING opportunities and (6) A PanNorthern approach: Their success is our success. Comments from recruiters provide insight and valuable information on their role in securing adequate health professionals in their area. CONCLUSION: Physician Recruiters have a very challenging job. They are expected to generate results with scant resources within a complex environment that poorly understands or values their role. Yet, their stories highlighted the relevance and joy in their challenges. INTRODUCTION: Au Canada, 18% de la population vit en milieu rural, mais seulement 8% des médecins en exercice travaillent dans ces régions. Les communautés rurales sont confrontées à des défis permanents en matière de recrutement et de maintien en poste des médecins et autres professionnels de la santé. L'amélioration de la qualité et de l'accès aux soins pour les Canadiennes et Canadiens vivant en milieu rural repose sur le recrutement et le maintien en poste de médecins exerçant en milieu communautaire. L'objectif de cette étude était d'explorer de manière approfondie les rôles, les expériences et les stratégies des recruteurs de médecins. Une fois mises en perspective, leurs histoires nous fournissent des informations précieuses provenant directement du terrain. MTHODES: À l'aide d'une approche fondée sur la théorie, nous avons mené des entrevues semi-structurées auprès de recruteurs de médecins du Nord de l'Ontario. RSULTATS: Douze recruteurs de médecins ont été interviewés. Six thèmes ont été identifiés: 1) Engagement communautaire: engagement avec un grand " E " et un petit " e ", 2) Rôle du recruteur: se connaître soi-même, connaître sa communauté, 3) Stratégies: tout est question de mouvement: aller vers les gens, tirer les ficelles et esquiver les obstacles, 4) les résultats: écouter, apprendre, assurer la relation, 5) les possibilités d'apprentissage, et 6) une approche pannordique: leur réussite est notre réussite. Les commentaires des recruteurs fournissent des informations précieuses sur leur rôle dans le recrutement de professionnels de la santé appropriés dans leur région. CONCLUSION: Les recruteurs de médecins ont un travail très exigeant. On attend d'eux qu'ils obtiennent des résultats avec des ressources limitées et dans un environnement complexe où leur rôle est mal compris ou peu valorisé. Pourtant, leurs témoignages ont mis en évidence la pertinence et la satisfaction que leur procurent ces défis.

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)0.001
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 score0.303
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.354
Teacher spread0.336 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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