A Participatory Approach to Understanding and Measuring Patient Satisfaction in a Primary Care Teaching Setting
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is a complex, multidimensional concept that is difficult to measure. However, there is agreement that understanding the expectations of a patient community or "what is important to them" is an essential consideration. We chose a participatory approach to address patient satisfaction in the context of a primary care teaching clinic. OBJECTIVES: The objectives of this project were to use a participatory research team of patients staff and researchers to (1) adapt an existing patient satisfaction questionnaire (PSQ) to the specific cultural and organizational elements ofa primary care teaching clinic, (2) administer the revised questionnaire and use the findings as a tool for organizational improvement, with the ultimate goal of increasing patient satisfaction, and (3) ensure that all decision making involved patients and staff to empower them in the process of organizational change. METHODS: We used an iterative, mixed methods approach to conduct this project. An interdisciplinary committee composed of members of the patient community, clinical and administrative staff, and researchers worked together as the primary decision making body. RESULTS: We modified a preexisting questionnaire to address the unique care delivery model of the clinic, issues of cultural sensitivity, and the need for simplified language and response format. Patient dissatisfaction was found to center on continuity and access to care. CONCLUSIONS: The participatory approach was critical to our success in understanding and measuring patient satisfaction from the patients' perspective. The involvement of the interdisciplinary committee and the high level of joint decision making in this project represents a unique contribution to assessing primary care patient 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 | 0.110 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 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".