Adaptation of a standardized self-reported cost questionnaire specific for the severe burn injury population (BI-CoPaQ)
Bibliographic record
Abstract
Severe burn injuries (SBIs) are known to pose a significant burden on patients, caregivers, and the healthcare system. Yet, scarce data on the short and long-term clinical and economic impacts of these injuries limit the development of evidence-informed strategies and policies to better care for these patients. To fill in this gap, we adapted a previously validated self-reported out-of-pocket cost measurement questionnaire, the Cost for Patients Questionnaire (CoPaQ), to the severe burn injury survivor context. We conducted one-on-one cognitive semi-structured interviews with burn injury survivors, their caregivers, and healthcare providers to identify elements of the CoPaQ's structure and content that needed to be revised to adapt to the specific health care trajectory, service utilization, needs and expenses incurred by adult severe burn injury survivors and their caregivers. Summative content analysis was used to identify items needing to be modified, deleted, or added. Based on this information, a preliminary version of a Burn Injury Cost for Patients Questionnaire (BI-CoPaQ) was developed and subsequently pre-tested on a small sample of SBIs survivors. Further validation of this tool will be required before BI-CoPaQ can be used as the standard for the estimation of the financial burden of SBIs in this population.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".