Perception of Healthcare Providers Regarding person-centered care for Burn Survivors
Bibliographic record
Abstract
Background: Burn are significant public health problems and account for a great proportion of survivors living with permanent disability so, healthcare providers play a vital role in delivering person-centered care for burn survivors, prioritizing their unique needs and preferences. Aim of the study: To assess the perception of healthcare providers regarding person-centered care for burn survivors. Research design: Descriptive Exploratory research design used in this study Setting: in-patient burn units, at Alwakra hospital, Qatar. Subject: purposive sample of 150 health care providers caring for inpatients burn survivors. Tools: tool I Participants assessment sheet, it included demographic and personal data, and tool II; Centered Practice Inventory – Staff, questionnaire. Results: The study findings revealed that as regarding prerequisite domain nearly half and more than half for both care environment and care process of health care provider were agreeing with the categories included in person centered care processes 48%, 52% and 52.7% receptively. Conclusions: the study findings concluded that the highest percentage of health care providers reported positive perception while slightly more than one quarter reported negative perception .Recommendations: Develop and implement regular training and professional development sessions focused on the core values of person-centered care, especially for areas with lower agreement such as the care environment domain.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".