Patients as strategic partners in hospital settings: Trust, participation, relational value, and loyalty
Bibliographic record
Abstract
Objective: The need to motivate patients to participate as strategic partners in healthcare exists, and this has prompted the development of relational models of value creation. This study assesses the effect of trust-in doctor/nurse on patient participation as well as the outcomes on perceived relational value and loyalty.Methods: An empirical model from the patient’s point of view was designed and tested. Data were collected from 209 patients, who had attended public (10) and private (10) hospitals/clinics, and analyzed using the principles of structural equation modelling.Results: The results show that patients’ perception of trust-in-doctor/nurse is an antecedent of patient participation. Patient participation has positive effects on perceived patient relational value, and this subsequently affects patient satisfaction, affective commitment, and loyalty.Conclusions: The study shows that trust affects patient participation behaviour, and the outcome of this behaviour contributes to value creation and loyalty in service delivery. Managerial implication: Nurses and doctors who build trust and involve patients create relational value with them, which enable patients to experience satisfaction and commitment, and this leads to long term relationships with the hospital. The study indicates that building trust and promoting patient participation should be a strategic imperative for management.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".