Causal Attributions and Health Behavior Choices Among Stroke and Transient Ischemic Attack Survivors
Bibliographic record
Abstract
To reduce the risk of a recurring event in patients who have suffered an initial stroke or transient ischemic attack (TIA), nurses are challenged with implementing and promoting changes in lifestyle and adherence to treatment regimens. Assessing patients' beliefs about the cause of the stroke or TIA is important to understanding their subsequent health behaviors. This study describes the causal attributions and health behavior choices of 9 participants following a stroke or TIA. Attributions were categorized as internal or external and cross-tabulated by controllability. The attributions were compared with health behavior choices. All participants attempted to make causal attributions, both internal (e.g., anxiety, hypertension, lifestyle) and external (e.g., stress, fate). Those making external attributions demonstrated poorer health behavior choices than those making internal attributions; controllability had no influence on behavior. Patients diagnosed more than 6 months before the study tended to make more external attributions. The results can help nurses understand the beliefs that drive the health behavior choices made by stroke and TIA survivors and guide them in tailoring prevention strategies and engaging patients in preventive activities.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".