Content validity of the Toronto Pain Management Inventory-Acute Coronary Syndrome Version.
Bibliographic record
Abstract
BACKGROUND: Cardiac pain and/or discomfort arising from acute coronary syndromes (ACS) can often be severe and anxiety-provoking. Cardiac pain, a symptom of impaired myocardial perfusion, if left untreated, may lead to further myocardial hypoxia, which can potentiate myocardial damage. Evidence suggests that once ACS patients are stabilized, their pain may not be adequately assessed. Lack of knowledge and problematic beliefs about pain may contribute to this problem. To date, no standardized tools are available to examine nurses' specific knowledge and beliefs about ACS pain that could inform future educational initiatives. AIM: To examine the content validity of the Toronto Pain Management Inventory-ACS Version (TPMI-ACS), a 24-item tool designed to assess nurses' knowledge and beliefs about ACS pain assessment and management. METHODS: Eight clinical and scientific experts rated the relevance of each item using a four-point scale. A content validity index was computed for each item (I-CVI), as well as the total scale, expressed as the mean item CVI (S-CVI/AVE). Items with an I-CVI > or = 0.7 were retained, items with an I-CVI ranging from 0.5-0.7 were revised and clarified, and items with an I-CVI < or = 0.5 were discarded. RESULTS: I-CVIs ranged from 0.5-1.0 and the S-CVI/AVE was 0.90, reflecting high inter-rater agreement across items. The least relevant item was eliminated. CONCLUSIONS: Preliminary content validity was established on the TPMI-ACS version. All items retained in the TPMI-ACS version met requirements for content validity. Further evaluation of the psychometric properties of the TPMI-ACS is needed to establish criterion and construct validity, as well as reliability indicators.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".