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Record W4307967178 · doi:10.2196/42033

Effectiveness of Shared Decision-making Training Programs for Health Care Professionals Using Reflexivity Strategies: Secondary Analysis of a Systematic Review

2022· review· en· W4307967178 on OpenAlexaffvenue
Ndeye Thiab Diouf, Angèle Musabyimana, Virginie Blanchette, Johanie Lépine, Sabrina Guay-Bélanger, Marie‐Claude Tremblay, Maman Joyce Dogba, France Légaré

Bibliographic record

VenueJMIR Medical Education · 2022
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité du Québec à Trois-RivièresCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité Laval
Fundersnot available
KeywordsReflexivityPsychological interventionHealth careIntervention (counseling)MedicineMedical educationNursingDecision aidsMEDLINESystematic reviewPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Shared decision-making (SDM) leads to better health care processes through collaboration between health care professionals and patients. Training is recognized as a promising intervention to foster SDM by health care professionals. However, the most effective training type is still unclear. Reflexivity is an exercise that leads health care professionals to question their own values to better consider patient values and support patients while least influencing their decisions. Training that uses reflexivity strategies could motivate them to engage in SDM and be more open to diversity. OBJECTIVE: In this secondary analysis of a 2018 Cochrane review of interventions for improving SDM by health care professionals, we aimed to identify SDM training programs that included reflexivity strategies and were assessed as effective. In addition, we aimed to explore whether further factors can be associated with or enhance their effectiveness. METHODS: From the Cochrane review, we first extracted training programs targeting health care professionals. Second, we developed a grid to help identify training programs that used reflexivity strategies. Third, those identified were further categorized according to the type of strategy used. At each step, we identified the proportion of programs that were classified as effective by the Cochrane review (2018) so that we could compare their effectiveness. In addition, we wanted to see whether effectiveness was similar between programs using peer-to-peer group learning and those with an interprofessional orientation. Finally, the Cochrane review selected programs that were evaluated using patient-reported or observer-reported outcome measurements. We examined which of these measurements was most often used in effective training programs. RESULTS: Of the 31 training programs extracted, 24 (77%) were interactive, among which 10 (42%) were considered effective. Of these 31 programs, 7 (23%) were unidirectional, among which 1 (14%) was considered effective. Of the 24 interactive programs, 7 (29%) included reflexivity strategies. Of the 7 training programs with reflexivity strategies, 5 (71%) used a peer-to-peer group learning strategy, among which 3 (60%) were effective; the other 2 (29%) used a self-appraisal individual learning strategy, neither of which was effective. Of the 31 training programs extracted, 5 (16%) programs had an interprofessional orientation, among which 3 (60%) were effective; the remaining 26 (84%) of the 31 programs were without interprofessional orientation, among which 8 (31%) were effective. Finally, 12 (39%) of 31 programs used observer-based measurements, among which more than half (7/12, 58%) were effective. CONCLUSIONS: Our study is the first to evaluate the effectiveness of SDM training programs that include reflexivity strategies. Its conclusions open avenues for enriching future SDM training programs with reflexivity strategies. The grid developed to identify training programs that used reflexivity strategies, when further tested and validated, can guide future assessments of reflexivity components in SDM training.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.393
GPT teacher head0.627
Teacher spread0.234 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2022
Admission routes2
Has abstractyes

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