Patient perspectives and hospital pay-for-performance: A qualitative study from Lebanon
Bibliographic record
Abstract
Abstract Background Patient perspectives have received increasing importance within health systems over the past four decades. Measures of patient experience and satisfaction are commonly used. However, these do not capture all the information available through patient engagement. An improved understanding of the various types of patient perspectives and the distinctions between them is needed. The lack of such knowledge limits the usefulness of including patient perspectives as components within pay-for-performance initiatives. This study was aimed to identify and explore patient perspectives on hospital care in Lebanon, and to describe how they relate to the national pay-for-performance initiative. Methods We conducted a qualitative study using focus group discussions with persons recently discharged after hospitalization under the coverage of the Lebanese Ministry of Public Health. This study was implemented in 2017 and involved 42 participants across eight focus groups. Qualitative content analysis was used to analyze the information provided by participants. Results Five overall themes supported by 17 categories were identified, capturing the meaning of the informants’ perspectives: health is everything; being turned into second class citizens; money and ‘wasta’ (personal connections) make all the difference; wanting to be treated with dignity and respect; and tolerating letdown, for the sake of right treatment. The most frequently prioritized statement in a ranking exercise regarding patient satisfaction was regular contact with one’s doctor. Conclusions Patient perspectives include more than what is traditionally incorporated in measures of patient satisfaction and experience. Patient valuing of health and their perceptions on each of the health system, and access and quality of care should also be taken into account. Hospital pay-for-performance initiatives can be made more responsive through a broader consideration of these perspectives. More broadly, health systems would benefit from wider engagement of patients. We propose a framework relating patient perspectives to value-based healthcare and health system performance.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".