Evaluation of an Online Course in 5 Languages for Inpatient Cardiac Care Providers on Promoting Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: Evidence proves that health care providers should promote cardiac rehabilitation (CR) to patients face-to-face to increase CR enrollment. An online course was designed to promote this at the bedside; it is evaluated herein in terms of reach, effect on knowledge, attitudes, discussion self-efficacy and practices, and satisfaction. METHODS: Design was observational, one-group pre- and post-test. Some demographics were requested from learners taking all language versions of the 20-min course: English, Portuguese, French, Spanish, and simplified Chinese, available at: https://globalcardiacrehab.com/CR-Utilization. Investigator-generated items in the pre- and post-test and evaluation survey administered using Google Forms were based on Kirkpatrick's training evaluation model. RESULTS: The course was initiated by 522 learners from 33 of 203 (16%) countries; most commonly female (n = 341, 65%) nurses (n = 180, 34%) from high-income countries (n = 259, 57%) completing the English (n = 296, 57%) and Chinese (n = 108, 21%) versions. A total of 414 (79%) learners completed the post-test and 302 (58%) completed the evaluation. Median CR attitudes were 5 of 5 on the Likert scale at pre-test, suggesting some selection bias. Mean CR knowledge ([7.22 ± 2.14]/10), discussion self-efficacy ([3.86 ± 0.85]/5), and practice ([4.13 ± 1.11]/5) significantly improved after completion of the course (all P < .001). Satisfaction was high regardless of language version ([4.44 ± 0.64]/5; P = .593). CONCLUSIONS: This free, open-access course is effective in increasing CR knowledge, self-efficacy, and encouragement practices among participating inpatient cardiac providers, with high satisfaction. While testing impact on actual CR use is needed, it should be more broadly disseminated to increase reach, in an effort to increase patient enrollment in CR, to reduce morbidity and mortality.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".