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Record W4234716286 · doi:10.17140/phoj-2-120

Patient Satisfaction with an Interprofessional Approach to Wound Care in Qatar

2017· article· en· W4234716286 on OpenAlexfundno aff
Shaikha Ali Al-Qahtani, Kim Critchley

Bibliographic record

VenuePublic Health - Open Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsPatient satisfactionMedicineWound careMEDLINENursingMedical emergencyFamily medicineIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

Background: Patient satisfaction with healthcare services is an important indicator of the patients' confidence in the healthcare system and a significant indicator of the quality of healthcare services delivered. This study assessed the level of patient satisfaction with wound care service delivery at the Hamad General Hospital (HGH) Outpatient Wound Clinic in Doha, Qatar. Methods: To complete this research a cross-sectional study design was conducted to survey patients who received wound care services from an interprofessional team at the HGH in Doha, Qatar from January 2015 to February 2016. Through this data collection method these patients' opinions on the services they received through the interprofessional approach were solicited. A total of 81 respondents completed a client satisfaction questionnaire (CSQ-8), 1 modified to include questions on socio-demographic characteristics. Data collection was completed from December 2015 to March 2016. Results: Overall, results from this study showed that patients were generally satisfied with wound care services delivered by an interprofessional team, as assessed by the CSQ-8. The results revealed favorable ratings of patient satisfaction ranging from 67.9% to 90.1%. Conclusions: Overall, study findings show that patients were mostly satisfied with wound care services and can be improved. A comparison of mean satisfaction scores by subgroups revealed no significant differences worth reporting.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.081
GPT teacher head0.400
Teacher spread0.319 · 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 designObservational
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

Citations2
Published2017
Admission routes1
Has abstractyes

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