How adults with cardiac conditions in Singapore understand the Patient Activation Measure (PAM-13) items: a cognitive interviewing study
Bibliographic record
Abstract
PURPOSE: Validation studies of the PAM-13 have found differences in scale performance, suggesting that health beliefs embedded in different cultures and/or self-management needs of different client groups influence how people respond to the items. The purpose of this study was to examine how adults with cardiac conditions in Singapore interpreted and responded to the PAM-13, to investigate possible reasons for differences in responses and to propose solutions to overcome them. METHODS: We conducted retrospective cognitive interviews with 13 participants in an out-patient heart center. Interviews were transcribed and analyzed based on the framework approach to qualitative analysis. The four stages from Tourangeau's cognitive model were used as a framework to index the data from each item. RESULTS: There was variation in comprehension of questions leading to variation in responses. Comprehension issues were due to terms perceived by participants to be vague and the use of English terms uncommon in Singapore. Cultural influences impacted decision processes and problems with response processes of the self-rating Likert scale surfaced. CONCLUSIONS: This study reinforces the need to culturally adapt the tool, even when language translation is not necessary. Providing Likert scales with a larger number of may widen the relevance of PAM-13 in Singapore. Implications for rehabilitation Need to culturally adapt assessment tool, even when language translation is not necessary. Consider using Likert scales with a larger number of categories when using in Asian countries such as Singapore. Caution must be taken when using PAM-13 levels to decide interventions for each individual.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".