A Comprehensive Tool to Support Family Physicians and Burn Survivors in the Provision of Long-term Burn Survivor Care
Bibliographic record
Abstract
Healthcare professionals increasingly recognize major burn injuries as a chronic condition due to their persistent and long-term health implications. The increasing survival rates and longer lifespans of burn survivors (BS) require general practitioners (GPs) to meet their long-term, potentially complex care. This project investigated the perceived need for a knowledge translation (KT) tool, and the content required, to optimize long-term support for BS by making best practices resources more accessible to their GPs. This mixed-method study assessed the perceived needs of BS and the current GP practices regarding BS follow-up. Survey data were gathered from GPs and BS. Additionally, a focus group was held with expert burn care clinicians. The results were analyzed using descriptive quantitative and qualitative methods, and the findings were triangulated. The common themes revealed the need for information about holistic care for BS across their illness trajectory. Ninety-four percent of GPs reported a lack of confidence treating BS who wanted information regarding comorbidities, psychosocial support, and symptom management, particularly related to scars (79%-94%). BS reported symptoms which interfere with their daily activities (ie, scar-related [75%], pain [57.5%], stiffness [52.5%], weakness [55%], fatigue [65%], psychosocial [55%], and cognitive issues [35%]). The KT tool generation was based on the amalgamated findings. This study revealed a consensus among BS, GPs, and expert clinicians that there was a need for a learning resource for GPs to support their role in providing BS long-term follow-up care. The resulting KT tool will enable GPs to bridge their knowledge gaps through user-friendly links to BS-relevant resources.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".