Patient Satisfaction with an Interprofessional Approach to Wound Care in Qatar
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".