Health Information Management Personnel Service Quality and Patient Satisfaction in Nigerian Tertiary Hospitals
Bibliographic record
Abstract
This study assessed the relationship between perceived service quality of health information management personnel and patient satisfaction in selected tertiary hospitals in Nigeria. A cross sectional survey was conducted with 280 patients from three tertiary hospitals in a Nigerian State. A self-administered questionnaire was distributed to outpatients who were literate, willing and attending the clinics for at least a second time. Perceived service quality was measured using a modified version of Service Quality (SERVQUAL) scale. Patient satisfaction was measured on a 4-point Likert-type scale developed by the researchers. Collected data were subjected to statistical analysis using mean, standard deviation and regression analysis. The surveyed patients were moderately satisfied with the services of the health information management personnel. Accordingly, patients’ perception of the health information management personnel service quality was found to be average. In addition, the research has shown that patients’ perception of health information management personnel service quality significantly influence their level of satisfaction in the studied tertiary hospitals (R = .62, F5,274 = 35.95, p = .000). Patient perceptions of service quality determine their overall satisfaction levels with the health information management personnel services. The tangible service quality dimension had more influence on patients’ satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".