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Record W3153909488 · doi:10.24908/iqurcp.10554

Evaluating a Motivational Interviewing Workshop for Medical Students at Queen’s University

2018· article· en· W3153909488 on OpenAlexvenueno aff
Sarah Skelding

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorMedical educationGeneral partnershipMotivational interviewingPsychologyInterviewMedicinePsychological interventionNursing

Abstract

fetched live from OpenAlex

Motivational interviewing (MI), a partnership-based counselling style, has increasingly been used to promote behaviour change among adults. Despite its potential to support behaviour change, there is a lack of research examining the most effective ways to train health care practitioners to use MI when working with patients. The purpose of this research was to evaluate uptake of MI skills following a one-day MI workshop that was developed in partnership with Exercise is Medicine for second year medical students enrolled at Queen’s University. The workshop focused on developing a basic understanding of the MI spirit, the phases of MI, and the ways in which MI may be used in different behavioural contexts. Participants (n=69) were asked to complete a pre and post-questionnaire that explored their knowledge of MI and their current perceptions of MI in addition to completing the Helpful Responses Questionnaire (HRQ), a validated MI assessment tool. A process evaluation was also conducted for each facilitator to measure fidelity to MI during the workshop. Results from the process and outcome evaluations are currently being evaluated. Results from this study will help determine whether a one-day workshop format is an appropriate delivery method for teaching MI techniques. Future research is needed to determine if medical students utilize information gained in their medical practice.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.390
GPT teacher head0.554
Teacher spread0.164 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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