Implementing recommendations for inpatient healthcare provider encouragement of cardiac rehabilitation participation: development and evaluation of an online course
Bibliographic record
Abstract
BACKGROUND: A policy statement recommending that healthcare providers (HCPs) encourage cardiac patients to enroll in cardiac rehabilitation (CR) was recently endorsed by 23 medical societies. This study describes the development and evaluation of a guideline implementation tool. METHODS: A stepwise multiple-method study was conducted. Inpatient cardiac HCPs were recruited between September 2018-May 2019 from two academic hospitals in Toronto, Canada. First, HCPs were observed during discharge discussions with patients to determine needs. Results informed selection and development of the tool by the multidisciplinary planning committee, namely an online course. It was pilot-tested with target users through a think-aloud protocol with subsequent semi-structured interviews, until saturation was achieved. Results informed refinement before launching the course. Finally, to evaluate impact, HCPs were surveyed to test whether knowledge, attitudes, self-efficacy and practice changed from before watching the course, through to post-course and 1 month later. RESULTS: Seven nurses (71.4% female) were observed. Five (62.5%) initiated dialogue about CR, which lasted on average 12 s. Patients asked questions, which HCPs could not answer. The planning committee decided to develop an online course to reach inpatient cardiac HCPs, to educate them on how to encourage patients to participate in CR at the bedside. The course was pilot-tested with 5 HCPs (60.0% nurse-practitioners). Revisions included providing evidence of CR benefits and clarification regarding pre-CR stress test screening. HCPs did not remember the key points to convey, so a downloadable handout was embedded for the point-of-care. The course was launched, with the surveys. Twenty-four HCPs (83.3% nurses) completed the pre-course survey, 21 (87.5%) post, and 9 (37.5%) 1 month later. CR knowledge increased from pre (mean = 2.71 ± 0.95/5) to post-course (mean = 4.10 ± 0.62; p ≤ .001), as did self-efficacy in answering patient CR questions (mean = 2.29 ± 0.95/5 pre and 3.67 ± 0.58 post; p ≤ 0.001). CR attitudes were significantly more positive post-course (mean = 4.13 ± 0.95/5 pre and 4.62 ± 0.59 post; p ≤ 0.05). With regard to practice, 8 (33.3%) HCPs reported providing patients CR handouts pre-course at least sometimes or more, and 6 (66.7%) 1 month later. CONCLUSIONS: Preliminary results support broader dissemination, and hence a genericized version has been created ( http://learnonthego.ca/Courses/promoting_patient_participation_in_CR_2020/promoting_patient_participation_in_CR_2020EN/story_html5.html ). Continuing education credits have been secured.
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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".