Changes in intention to use an interprofessional approach to decision-making following training: a cluster before-and-after study
Bibliographic record
Abstract
BACKGROUND: Health professionals in home care work in interprofessional teams. Yet most training in decision support assumes a one-on-one relationship with patients. We assessed the impact of an in-person training session in interprofessional shared decision-making (IP-SDM) on home care professionals' intention to adopt this approach. METHODS: We conducted a secondary analysis of a cluster stepped-wedge trial using a before-and-after study design. We collected data among home care professionals from November 2016 to February 2018 in 9 health and social services centers in Quebec, Canada. The intervention was an in-person IP-SDM training session. Intention to engage in IP-SDM pre- and post-session (dependent variable) was compared using a continuing professional development evaluation scale (CPD-Reaction) informed by the Godin's Integrated Behavioral Model for health professionals. We also assessed socio-demographic and psychosocial variables (beliefs about capabilities, beliefs about consequences, social influence and moral norm). We performed bivariate and multivariate analysis to identify factors influencing post-intervention intention. We used the STROBE reporting guidelines for observational studies to report our results. RESULTS: Of 134 respondents who provided complete pairs of questionnaires (pre- and post-), most were female (90.9%), mean age was 42 (± 9.3) years and 66.9% were social workers. Mean intention scores decreased from 5.84 (± 1.19) to 5.54 (± 1.35) (Mean difference = -0.30 ± 1.16; p = 0.02). Factors associated with higher intention post-intervention were social influence (ß = 0.34, p = 0.01) and belief about capabilities (ß = 0.49, p < 0.01). CONCLUSION: After in-person IP-SDM training, healthcare professionals' intention to engage in IP-SDM decreased. However, the scope of this decrease is probably not clinically significant. Due to their association with intention, beliefs about capabilities, which translate into having a sense of self-competency in the new clinical behavior, and social influences, which translate into what important others think one should be doing, could be targets for future research aiming to implement IP-SDM in home care settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".