[Patterns of client complaints in Israel's healthcare system].
Bibliographic record
Abstract
OBJECTIVES: Israel's National Health Law of 1994 established the rights of citizens to receive quality health services. The Law set several mechanisms for treating clients' complaints. This study examined complaint patterns and reasons, complaint channels, and knowledge regarding complaints among citizens insured by Israeli health services, with the goal of contributing to the usage of complaints as a tool for improving medical services to citizens. METHOD: The study was conducted using a telephone survey, with national probability sample of N = 1500 respondents aged 21+. FINDINGS: About 75% of respondents did not have a cause to complain over the last 12 months, 25% reported a cause to complain, but only 9.5% (143 of 1500) actually complained. Approximately half of the causes for complaint concerned structural problems such as payments, waiting lists or eligibility. Causes regarding processes and interactions, or medical treatment and its outcomes, each accounted for a quarter of causes. Most complainers, about 75%, submitted their grievances informally at the local level. Only a minority (17%) appealed to official bodies established by Israel's National Health Law at the Ministry of Health or at the HMOs. Clients' awareness regarding rights to complain was found to be low. CONCLUSIONS: There is a need to improve clients' knowledge and change complaint patterns, develop means for analyzing existing knowledge about client complaints already available to front-line employees. Furthermore, there is a need to plan organizational mechanisms for capturing and using future complaints submitted on a local level or informally, which are the bulk of the complaints.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".