Knowledge, attitudes and behaviours of cardiac health professionals towards cognitive screening in acute coronary syndrome patients
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background Cognitive impairment (CI) is common in acute coronary syndrome (ACS) patients but is often undetected and may impact recovery and secondary prevention uptake. Health professionals play a vital role in the early detection of CI through screening and managing CI in ACS patients. Purpose To explore health professionals’ knowledge, attitudes, and behaviours toward CI screening in ACS patients Methods Cardiac health professionals were recruited via acute and outpatient cardiac wards in three metropolitan teaching hospitals and from emailing members of two cardiac professional associations in Australia. All completed a 38-item survey administered in either paper or electronic format. Results 100 health professionals responded (95 nurses and five allied health workers). Respondents identified the prevalence of CI, dementia, or delirium at 25% post-ACS (50% of respondents), and 74% identified difficulties in recalling recent information as the most common indicator of CI. The cognitive screening was performed at least some of the time in ACS patients by 73%. After accounting for age, receiving training in CI, work experience, and profession, cognitive screening was conducted more than eight times more often by health professionals who work in acute settings (OR=8.78, 95%CI 2.13, 36.25) versus non-acute. Participants identified the main reasons for conducting cognitive screening as early detection of change/establishing a baseline (n=27) and when they suspected any cognitive issue or decline (n=26). The most common barriers to both screening for CI and taking further actions when CI was detected were patients being unable to communicate well (60% and 49%), patients being too unstable/unwell (59% and 42%), and the priority being the patient’s clinical care (53% and 44%). Conclusions Health professionals working in acute settings are most likely to screen for CI regardless of experience or training in CI, leaving CI likely to be undetected in ACS patients receiving care in other settings. Barriers to screening are common and challenging to address due to time shortages and the appropriateness of tools. A standardised screening guideline and more feasible screening tools are needed to overcome the barriers to cognitive screening in ACS patients. Pre-professional education should also be implemented in the future.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".