MétaCan
Menu
Back to cohort
Record W4224881952 · doi:10.7565/ssp.v5.6698

Acting as a Change Agent

2022· article· en· W4224881952 on OpenAlexafffund
Virginie Savaria, Emmanuelle Jasmin, Anne Hudon, Denis Bédard, Marie-Josée Drolet, Michael F. Beaudoin, Étienne Lavoie-Trudeau, Annie Carrier

Bibliographic record

VenueSocial Science Protocols · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationUniversité de SherbrookeMontfort HospitalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité du Québec à Trois-RivièresCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersFaculty of Medicine and Health, University of SydneySocial Sciences and Humanities Research Council of CanadaUniversité de Sherbrooke
KeywordsChecklistGeneral partnershipMedical educationPsychologyProfessional developmentHealth professionalsTraining and developmentKnowledge managementBusinessProcess managementMedicineHealth careComputer sciencePolitical scienceManagement

Abstract

fetched live from OpenAlex

Background: Acting as a change agent (CA) is a key role for Health and Social Services (HSS) professionals. It involves working collaboratively with actors across and outside the HSS system and influencing decision-makers. However, this role requires specific skills that HSS professionals generally feel that they have not mastered. The overarching goal of this research partnership is to explore the development of CA skills by HSS professionals using a customized training program.
 Methods/Design: Through a research partnership, 128 HSS professionals will receive 7 hours of training using a professional co-development approach and a checklist. The immediate and medium-term effects of the training on their skills development will be evaluated with a self-administered questionnaire before and immediately following the training and again nine months later. The data will be analyzed using descriptive and inferential statistics.
 Discussion: This study will shed light on the effects of a customized training program on CA skills development. It will also have three main benefits: (1) development of an easy-to-reuse CA training program and checklist; (2) partner’s ownership of these products through close involvement; and (3) development of a sustainable partnership between a team of researchers and a recognized organization with an extensive HSS network.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.928
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.624
GPT teacher head0.592
Teacher spread0.032 · 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
GenreProtocol

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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueSocial Science ProtocolsSame topicMental Health and Patient InvolvementFrench-language works237,207