Impact of intensive training on health professionals’ self-efficacy in establishing, running and maintaining a cardiac rehabilitation program
Bibliographic record
Abstract
We developed an intensive five-day training program for health professionals working in cardiac rehabilitation (CR). The training covers topics related to establishing, running, maintaining and evaluating a CR program. The aim of this study was to assess the impact of the training on health professionals’ self-efficacy regarding the effective delivery of CR. From 2014 to 2018, 167 health professionals participated in one of five training programs. Participants completed a 28-item pre- and post-training self-efficacy scale. For a sub-group, self-efficacy was re-assessed 4 months later. Factor analysis was used to identify self-efficacy domains. Paired sample t-tests compared pre- and post-training self-efficacy scores; repeated measures analysis of variance investigated change over the three time points. Variations in self-efficacy across profession, role in CR, and years of CR practice were investigated. Factor analysis identified three domains: Operational aspects of CR; Medical aspects of heart disease; and Psychosocial aspects of CR. Health professionals’ self-efficacy increased significantly after training participation, across the three domains and for the total score. Effects were sustained in the 4-month follow-up. Few variations in self-efficacy trajectories by participant characteristics were identified. The study demonstrates that our health professional CR training significantly improves health professionals’ confidence in a range of areas related to establishing, running, maintaining and evaluating a CR program, with immediate improvements sustained four months later. The pattern of findings was largely consistent regardless of participants’ role and experience in CR. Findings highlight the benefits of this relatively brief intensive program on enhancing the capacity of the CR workforce.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".