Patient assessment of the quality of dental care services in a Nigerian hospital
Bibliographic record
Abstract
Dental care services are available in many urban communities worldwide where discerning and sophisticated clients expect quality care. Many available studies evaluated satisfaction rather than quality of dental care; others did not reveal the patients’ perception of gaps in the quality of care. Service quality (SERVQUAL) tool assesses quality of service based on the dimensions of tangibles, reliability, responsiveness, assurance and empathy as described by Parasuraman et al. (1985). The aim of this study was to assess the gaps in quality of dental care in a Nigerian government owned dental clinic using an unweighted SERVQUAL tool to determine the difference between expectations and perceptions of patients. Consenting patients seen during the study period were given a 32-items questionnaire divided equally between expectations and perception of quality of dental care services received. Out of 112 questionnaires analysed, patients had the most expectation for neatness (4.69 ± 0.85) and least for pain free treatment (3.76 ± 1.16). Highest perception was for knowledgeable clinic staff (4.34 ± 0.71) while support to enable staff work well was the least perceived quality (3.73 ± 0.86). Overall, among the 5 dimensions of quality, there were marked statistically significant quality gaps in assurance (p = .0001) and tangibles (p = .0006). This study showed that patients in a Nigerian government-owned dental clinic, there is need for greater attention to be paid to assurance, tangibles and reliability dimensions of service quality to improve patient perceptions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".