Healthcare providers' intentions to engage in an interprofessional approach to shared decision-making in home care programs: A mixed methods study
Bibliographic record
Abstract
In an interprofessional approach to shared decision-making (IP-SDM), an interprofessional team collaborates in identifying best options and helps patients determine their preferences, enabling them to take more control over the treatment plan. However, little is known about fostering IP-SDM in Canada's healthcare system. Therefore, we sought to evaluate health professionals' intentions to engage in IP-SDM in home care and explore the factors associated with this intention. A total of 272 eligible home care providers completed a questionnaire based on the theory of planned behavior. Eight managers and one healthcare team caring for the frail elderly were interviewed about possible barriers and facilitators. Analysis involved descriptive statistics and multivariate analysis of quantitative data and content analysis of qualitative data. On a scale of - 3 (strongly disagree) to +3 (strongly agree), the mean intention to engage in IP-SDM was positive (1.42 ± 1.39). The intention was influenced by the following theory-based determinants (R(2) = 57%; p ≤ 0.002), i.e. cognitive attitude (p < 0.001) subjective norm (p < 0.0001) and perceived behavioral control (p < 0.0001), with variations depending on the type of provider. Barriers included lack of time, poor team cohesion and high staff turnover. Facilitators included team cohesion and shared tools. Future programs implementing IP-SDM could address these barriers and facilitators.
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 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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".