[THE FUTURE OF HOSPITALS IN ISRAEL - PLANNING CONSIDERATIONS AND THE STANDPOINTS OF MANAGERS].
Bibliographic record
Abstract
INTRODUCTION: Planning the future national hospitalization system requires consideration of demographic trends, innovative treatments and policy approaches. The existing situation alone does not allow proper planning in extremely dynamic systems that operate within the framework of scarce resources. OBJECTIVES: To identify managers' attitudes regarding hospital planning, deployment and managerial mechanisms in comparison with evidence in the literature. METHODS: A survey among hospital managers following a focused conference. RESULTS: Of the 50 respondents, half of the group thought that a general hospital should include 900-2000 beds. The majority prefer an autonomous management style, or a cluster of only a few hospitals. In a scenario of overload and shortage of beds, the majority prefer adding beds to the existing hospital, while about a quarter of the respondents suggest establishing another hospital in the area, or merging nearby hospitals. About half supported home care, or transferring patients to a nearby hospital, including in the private sector, or the transfer of appropriate patients to chronic care institutions. About a third of the respondents supported telemedicine. In terms of national deployment, the preference was that the hospital should be located in high population areas and able to provide sufficient geographical accessibility. Yet, 60% of participants emphasized the importance of social determinants to low socio-economic populations. CONCLUSIONS: The survey revealed original standpoints and ideas towards willingness to promote targeted solutions. Healthcare leaders should consider and adapt local ideas to achieve effective planning following the insights of those working in the field. DISCUSSION: Targeted conferences aimed at discussing health policy are an effective platform for presenting complex issues and for sharing ideas with colleagues for the benefit of meaningful long-term processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".