A training program designed to improve interprofessional knowledge, skills and attitudes in chronic disease settings
Bibliographic record
Abstract
For over 25 years, The Arthritis Program (TAP) at Southlake Regional Health Centre has worked within a successful interprofessional model. TAP recognized the need to teach its model and developed The Arthritis Program - Interprofessional Training Program (TAP-ITP). This pilot study evaluated perceptions of 22 TAP-ITP participants related to effectiveness and satisfaction. The study employed a longitudinal survey design and data were collected at the baseline (T1), post-program (T2), and at one year (T3) by use of the following instruments: W(e)Learn Program Assessment; Interprofessional (IP) Learner and Team Contracts; Interprofessional Collaborative Competencies Attainment Survey (ICCAS); Bruyère Clinical Team Self Assessment Scale; and Attitudes Toward Health Care Teams (ATHCT). Data analysis included descriptive, non-parametric and parametric tests. Results indicated participants were very satisfied with TAP-ITP. ICCAS scores revealed statistically significant differences (Wilcoxon rank sum tests) from T1 to T2 in perceptions of IPC competencies (p < 0.05). Paired t-tests for each T1 to post (T2 and T3) scores were all significant (p < 0.05) for each Bruyère subscale and overall scores. For ATHCT, paired t-tests for each T1 to T2 were significant for Quality of Care/Process (p = 0.04) and borderline significant for Physician Centrality scale (p = 0.06). At T3, improvement in both scales was maintained. This pilot study suggests that TAP-ITP improves self-assessed scores of knowledge and skills, as well as attitudes in interprofessional care post-program and sustained at one year.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".