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Record W4399718182 · doi:10.52609/jmlph.v4i3.130

Evaluating Patient Satisfaction With Nurse-Led Wound Care Services

2024· article· en· W4399718182 on OpenAlexvenueno aff
Ayat AlZayed, Diana S. Lalithabai

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsNursingWound carePatient satisfactionMedicinePsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: The increasing incidence of chronic wounds, combined with the high number of patients requiring hospital services, has led to the concept of nurse-led wound care clinics to support general practitioners in the treatment and management of wounds. Aim: This study aims to assess patients’ perception of, and satisfaction with, wound care services in a tertiary healthcare setting in Riyadh, Saudi Arabia. Methods: The study utilised a cross-sectional descriptive design and was conducted between September 2022 and October 2023, and data were collected via a client satisfaction questionnaire (CSQ-8). Results: Our findings revealed very positive responses overall. Considered together (response options 4 and 3), a majority of respondents (91.3%) rated the quality of service they received as “excellent” or “good”, and 85.6% reported receiving the kind of service they wanted. Regarding overall satisfaction, 92.5% of respondents reported being “very satisfied” or “mostly satisfied” with the overall service they received. Conclusion: This study reveals positive patient satisfaction with overall wound care services. However, there remains weakness in certain areas. This could be understood in more detail by conducting a qualitative study, so that action maybe taken to further improve the quality of healthcare services provided to patients.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.489
Teacher spread0.366 · 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.

Study designQualitative
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
Published2024
Admission routes1
Has abstractyes

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