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Record W3090076553

Separate Estates: A Case Study Analysis of Competency Assessment Processes among Clinicians in a Canadian Academic Hospital

2019· dissertation· W3090076553 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
FundersUniversity of TorontoCanadian Nurses Foundation
KeywordsMedicineFamily medicineMedical education
DOInot available

Abstract

fetched live from OpenAlex

This study examined competence and competency assessment of regulated health professionals (RHPs) through a case study approach that utilized the Boyatzis model of effective performance. In order to explore how competency assessment was enacted and understood, and how RHPs perceived and experienced competency assessment in a large Canadian academic hospital, the study used three sources of data: key informant interviews (n=19), focus group interviews (n=32), and a review of organizational documents. Participants included RHPs, managers, directors, executives, and other non-health participants, such as human resources professionals. Data were analyzed inductively using Braun and Clark’s (2006) theoretical thematic analysis, and deductively using Hsieh and Shannon’s (2005) directed content analysis, all in NVivo Version 11 for Windows. Data revealed the absence of common language for competence across the hospital’s RHP participants. Participants referred to regulatory concepts and competency frameworks when asked to define and describe competence or competency assessment in the hospital setting. At the same time, a strong organizational rhetoric around excellence was evident, as was frequent commentary on incompetence. There was little discussion on RHPs who fell between excellence and incompetence. When describing organizational competency assessment processes, participants were equivocal about what was assessed and whether this assessment focused on organizational values or practice competencies. Fundamental differences in competency assessment processes exist for unionized RHP employees, non-unionized RHP employees and appointed physicians who were not employees, creating a situation of “separate estates”. These estates made interprofessional collaboration difficult by reinforcing uniprofessional perspectives, preventing organizational level discussions of competence and limiting the organization’s ability to harmonize processes across RHPs. Competence did not feature as a prominent concern for most participants who expressed the view that as long as the individual remained in good standing with their regulatory body, competency was not a matter of organizational concern. Recommendations call for healthcare organizations to implement best practice approaches to competency assessment across all RHPs; to establish congruence between organizational and regulatory assessments of competence; and to align assessment language between healthcare workplaces and regulatory bodies in order to facilitate meaningful discussions concerning competence in the practice setting and address the issue of “separate estates”.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0260.007
Scholarly communication0.0050.002
Open science0.0040.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.395
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2019
Admission routes2
Has abstractyes

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