MétaCan
Menu
Back to cohort
Record W4407154107 · doi:10.1097/ceh.0000000000000595

Applying the Purpose, Autonomy, Confidence, Engrossment Model of Motivational Design to Support Motivation for Continuing Professional Development

2025· article· en· W4407154107 on OpenAlexafffund
Adam Gavarkovs, Danielle Glista, Robin O’Hagan, Sheila Moodie

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2025
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British ColumbiaWestern University
FundersEmployment and Social Development Canada
KeywordsPaceAutonomySelf-determination theoryPsychologyKnowledge managementMedical educationInfographicHealth professionalsApplied psychologyHealth careMedicineComputer science

Abstract

fetched live from OpenAlex

ABSTRACT: Health professionals' motivation is a key determinant of their continuing professional development (CPD) outcomes. Therefore, CPD providers must ensure that they design CPD activities to support health professionals' motivation; this process is referred to as motivational design. The aim of this article is to introduce CPD providers to the PACE (purpose, autonomy, confidence, engrossment) model of motivational design, and describe how we applied the PACE model to create two online modules for an interprofessional audience. The PACE model builds on other available models of motivation design by offering theoretically informed strategies to support autonomous motivation, a specific quality of motivation that is associated with more effective learning processes and outcomes. Our experience suggests that CPD providers can use the PACE model to guide their motivational design efforts. We also encourage CPD researchers to test the theoretical assumptions that inform the PACE model.

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)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.115
GPT teacher head0.484
Teacher spread0.369 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Continuing Education in the Health ProfessionsSame topicInnovative Teaching and Learning MethodsFrench-language works237,207