Applying justice and commitment constructs to patient-health care provider relationships.
Bibliographic record
Abstract
OBJECTIVE: To examine patients' experiences of fairness and commitment in the health care context with an emphasis on primary care providers. DESIGN: Qualitative, semistructured, individual interviews were used to gather evidence for the justice and commitment frameworks across a variety of settings with an emphasis on primary care relationships. SETTING: Rural, urban, and semiurban communities in Nova Scotia. PARTICIPANTS: Patients (ages ranged from 19 to 80 years) with varying health care needs and views on their health care providers. METHODS: Participants were recruited through a variety of means, including posters in practice settings and communication with administrative staff in clinics. Individual interviews were conducted and were audiotaped and transcribed verbatim. A modified grounded theory approach was used to interpret the data. MAIN FINDINGS: Current conceptualizations of justice (distributive, procedural, interpersonal, informational) and commitment (affective, normative, continuance) capture important elements of patient-health care provider interactions and relationships. CONCLUSION: Justice and commitment frameworks developed in other contexts encompass important dimensions of the patient-health care provider relationship with some exceptions. For example, commonly understood subcomponents of justice (eg, procedural consistency) might require modification to apply fully to patient-health care provider relationships. Moreover, the results suggest that factors outside the patient-health care provider dyad (eg, familial connections) might also influence the patient's commitment to his or her health care provider.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".