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Record W2101192057 · doi:10.3109/01421591003686237

Mentorship for the physician recruited from abroad to Canada for rural practice

2010· article· en· W2101192057 on OpenAlexafffundabout
Jocelyn Lockyer, Herta Fidler, Christopher de Gara, James Keefe

Bibliographic record

VenueMedical Teacher · 2010
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersMinistry of Advanced Education, Government of Alberta
KeywordsMentorshipEnculturationMedical educationFace (sociological concept)Perspective (graphical)MedicineFocus groupPsychologyNursingPedagogySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Mentoring is one way to help physicians new to a country assimilate. AIM: This study examined the feasibility and focus of a mentoring program from the perspective of medical leaders (leaders) and physicians new to Canada (physicians). METHODS: Focus groups with 23 physicians were held in six regional centers. Face-to-face interviews were held with 10 leaders. They were asked to discuss how a mentoring program might be helpful and how a program might be designed and evaluated. RESULTS: Both leaders and physicians recognized that mentorship would support the physician socially, professionally, and emotionally. They told us that mentorship programs should be structured carefully to build trust, allow mentors and mentees some selection, be in geographic proximity where possible, and have transparent rules. While leaders felt that evaluation would be an important part of a mentorship program, the physicians disagreed noting that it would change the nature of the program. Leaders stated that the ultimate evaluation of the program's success would be found in retention numbers. CONCLUSION: Physicians new to a country need support. Mentorship is a feasible approach to support new comers that may result in more efficient and effective integration, enculturation, and higher levels of retention.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.037
GPT teacher head0.381
Teacher spread0.344 · 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

Citations8
Published2010
Admission routes3
Has abstractyes

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