Alternative Coping Strategies and Decision Delay in Seeking Care for Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In attempting to manage acute myocardial infarction (AMI) symptoms, individuals often engage in coping strategies that cause them to delay seeking timely medical care. Review of the literature revealed that several coping strategies were examined in relation to AMI delay, but there were no studies investigating the extent of use of each coping strategy. The purpose of this study was to examine the extent of use of 15 coping strategies and their associations with the time taken to decide to seek medical care. SUBJECTS AND METHODS: Secondary data analyses were performed on a sample of 135 patients with AMI from Canada and the United States from whom data were collected via structured interviews. Descriptive analyses were used to identify the extent of use of alternative coping strategies. Spearman rho was used to test the associations of these strategies with decision delay. RESULTS AND CONCLUSIONS: The 3 most frequently used coping strategies were trying to relax, wishing/praying for symptoms to disappear, and discussing symptoms with someone. Nine coping strategies were significantly associated with decision delay. The findings of this study are useful in teaching patients about avoiding the use of coping strategies when faced with AMI symptoms. This can potentially help reduce delays in seeking care for AMI, which will result in better health outcomes for patients with AMI.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".