Feasibility of a Virtual Educational Programme for Behaviour Change in Cardiac Patients from a Low-Resource Setting
Bibliographic record
Abstract
Patient education is an integral part of recovery from a critical cardiac life event and a core component of cardiac rehabilitation (CR) programmes. This study addressed the feasibility of a virtual educational programme for behaviour change in CR patients from a low-resource setting in Brazil. Cardiac patients from a CR programme closed due to the pandemic received a 12-week virtual educational intervention (WhatsApp messages and bi-weekly calls from healthcare providers). Acceptability, demand, implementation, practicality, and limited efficacy were tested. Overall, 34 patients and 8 healthcare providers agreed to participate. The intervention was considered practical and acceptable by the participants, who reported a satisfaction median of 9.0 (7.4-10.0)/10 (patients) and 9.8 (9.6-10.0)/10 (providers). The main difficulties in carrying out the intervention activities were related to technology, motivation to self-learning, and a lack of in-person orientation. All the patients reported that the information included in the intervention was aligned with their information needs. The intervention was associated with changes in exercise self-efficacy, sleep quality, depressive symptoms, and performance of high-intensity physical activity. In conclusion, the intervention was considered feasible to educate cardiac patients from a low-resource setting. It should be replicated and expanded to support patients that face barriers to onsite CR participation. Challenges related to technology and self-learning should be addressed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".