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Record W2085873618 · doi:10.1080/13561820902892871

Identification of facilitators and barriers to the role of a mentor in the clinical setting

2009· article· en· W2085873618 on OpenAlexaffabout
Roberta Heale, Sharolyn Mossey, Bev Lafoley, Robyn Gorham

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsLaurentian University
Fundersnot available
KeywordsVariety (cybernetics)Medical educationHealth careMedicineClinical PracticeConsistency (knowledge bases)NursingIdentification (biology)Psychology

Abstract

fetched live from OpenAlex

Clinical mentors are integral to the education of health care professionals. In Northeastern Ontario, Canada, clinical mentors can take a variety of forms. Examples include preceptors who are employees in a clinical setting working with an individual student for a specific period of time, clinical educators, individuals contracted to take a group of students in acute care settings, and faculty advisors, who facilitate students' community placements. An internet survey exploring the preparation and support of clinical mentors was delivered to clinical mentors from a variety of health disciplines. Part of the survey was based on the concept of self-efficacy which assessed participants' confidence levels with the various aspects of the clinical mentor role. Participants also reported on supports and barriers to their role as clinical mentors. Findings indicate that clinical mentors across all the health disciplines are not always confident in the delivery of clinical education, most specifically with adapting teaching style and assisting the student to apply research to practice. Consistency of results of the survey speaks to the potential value for a collaborative, interprofessional approach to the orientation and support of clinical mentors in a variety of health discipline education programs.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.390
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 integrity0.0000.000
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 head0.011
GPT teacher head0.404
Teacher spread0.393 · 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

Citations36
Published2009
Admission routes2
Has abstractyes

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