Abstract No: 220 Assessing Information Needs in Cardiac Patients from A Tertiary Care Hospital
Bibliographic record
Abstract
Purpose: Patient education is a core component of phase 2 cardiac rehabilitation. It is important to educate patients with the information that they need for safety and secondary prevention. Understanding what patients need and providing specific information may promote better adherence and hence outcomes. Participants: Adult Cardiac patients admitted to the ward and awaiting intervention or those discharged, or relatives of the cardiac patients who were willing to fill out the online Information Needs survey were approached in the period from 22 June 2022 to 10 November 2023. Methods: The Information Needs in Cardiac Rehabilitation (INCR) scale comprises 55 items, assessing needs in 10 areas. Items are each scored on a 5-point Likert scale, with higher scores indicating greater need;. In this cross-sectional sub-study of a larger global ICCPR initiative, the online survey which was prefaced by sociodemographic items was administered by physiotherapists. Analysis: Descriptive statistics. Results: 84 males and 33 females participated in the survey. The mean age of the participants was 50.22 years. Of the 117 participants, 64.66% had started cardiac rehab, and 31.62% were on disability (sick leave) or modified duties at work. The greatest information needs in the cohort were reported as: What medications do I need for my heart? (4.43); How will exercise help my heart condition? (4.40); What happens when someone has a heart attack or other heart event? (4.40); When should I see the doctor or go to the emergency room? (4.39); and What should I do if I feel angina or chest pain? (4.38). Conclusion: Information regarding medications, emergency care, and safety actions in response to chest pain were the most important needs in this population. Implications: Physiotherapists should fulfill these information needs using best practices and evidence-based lay materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".