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Perception of Healthcare Providers Regarding person-centered care for Burn Survivors

2025· article· en· W4415553869 on OpenAlexaboutno aff
Sahra Zaki Azer, Manal Sayed Atya

Bibliographic record

VenueAssiut Scientific Nursing Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHealth carePerceptionExploratory researchDescriptive researchQuarter (Canadian coin)Nonprobability samplingSample (material)Descriptive statistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.342
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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