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Record W3109096955 · doi:10.1111/medu.14422

Incentives for clinical teachers: On why their complex influences should lead us to proceed with caution

2020· article· en· W3109096955 on OpenAlexafffund
Katherine Wisener, Erik W. Driessen, Cary Cuncic, Cassandra L. Hesse, Kevin W. Eva

Bibliographic record

VenueMedical Education · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
FundersRoyal College of Physicians and Surgeons of Canada
KeywordsIncentiveThematic analysisAttritionContext (archaeology)PsychologyProcess (computing)Medical educationPublic relationsQualitative researchSocial psychologyMedicinePolitical scienceSociologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

INTRODUCTION: When medical education programs have difficulties recruiting or retaining clinical teachers, they often introduce incentives to help improve motivation. Previous research, however, has shown incentives can unfortunately have unintended consequences. When and why that is the case in the context of incentivizing clinical teachers remains unclear. The purposes of this study, therefore, were to understand what values and motivations influence teaching decisions; and to delve deeper into how teaching incentives have been perceived. METHODS: An interpretive description methodology was used to improve understanding of the development and delivery of teaching incentives. A purposeful sampling strategy identified a heterogenous sample of clinical faculty teaching in undergraduate and postgraduate contexts. Sixteen semi-structured interviews were conducted and transcripts were analyzed using an iterative process to develop a thematic structure that accounts for general trends and individual variations. RESULTS: Clinicians articulated interrelated and dynamic personal and environmental factors that had linear, dual-edged and inverted U-shaped impacts on their motivations towards teaching. Barriers were frequently rationalized away, but cumulative barriers often led to teaching attrition. Clinical teachers were motivated when they felt valued and connected to their learners, peers, leadership, and/or the medical education community. While incentives aimed at producing these connections could be perceived as supportive, they could also negatively impact motivation if they were impersonal, inequitable, inefficient, or poorly framed. DISCUSSION/CONCLUSION: These findings reinforce the literature suggesting that it is necessary to proceed with caution when labeling any particular factor as a motivator or barrier to teaching. They take us deeper, however, towards understanding how and why clinical teachers' perceptions are unique, dynamic and fluid. Incentive schemes can be beneficial for teacher recruitment and retention, but must be designed with nuance that takes into account what makes clinicians feel valued if the strategy is to do more good than harm.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.339
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.143
GPT teacher head0.465
Teacher spread0.322 · 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

Citations32
Published2020
Admission routes2
Has abstractyes

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