Relationship between Quality of the Referral Chain of Hospital Services and Patient Satisfaction
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to examine the relationship between quality of the referral chain of hospital services and patient satisfaction in Kerman, Iran. METHODS: The study was quantitative-qualitative and crosses-sectional, conducted during August-October, 2015 in a hospital in Kerman, Iran. Two questionnaires were used in the study. During data collection, 115 patients were referred from a public hospital to an imaging center for imaging services that quality of the referral chain and satisfaction were studied. The quality of referral change was completed by the head nurses of the hospital. Satisfaction from referral service was assessed by the patient. In order to investigate the correlation between patient satisfaction and the supply chain quality and its domains the Pearson correlation test was conducted. FINDINGS: The average score of quality of the referral chain was 4.53 out of 5 with a standard deviation of 0.45. The average score of patient satisfaction from the referred services was 4.12 out of 5, with a standard deviation of 0.52. “Provision of information to patients” got the highest average and “effective communication” the least among the dimensions of quality of the referral chain. There was a significant relationship between the quality of referral chain and patient satisfaction from the referred services. CONCLUSION: Participation of all suppliers in the supply chain can create an atmosphere of sincere cooperation between the organizations. Long-term plans should be established to set cooperation among supply chain parties, i.e. hospitals and satellite labs and imaging centers.
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 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 | 0.009 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".