How to improve the organisation of Mental Healthcare for older adults in Belgium?
Bibliographic record
Abstract
1. SEARCH STRATEGY OF THE NARRATIVE SCOPING REVIEW 4 -- 1.1. RESEARCH QUESTION 4 -- 1.2. PICO 4 -- 1.3. DEFINITIONS 4 -- 1.4. POTENTIAL SEARCH TERMS 5 -- 2. SEARCH STRATEGY 6 -- 2.1. MEDLINE 6 -- 2.2. COCHRANE DATABASE OF SYSTEMATIC REVIEWS 7 -- 2.3. CINAHL 9 -- 2.4. EMBASE 10 -- 3. ONLINE SURVEY TO STAKEHOLDERS 12 -- 3.1. DUTCH VERSION 12 -- 3.2. FRENCH VERSION 19 -- 4. COMPARATIVE STUDY OF FIVE COUNTRY’S APPROACHES TO THE IMPLEMENTATION OF MENTAL HEALTH CARE FOR THE ELDERLY 26 -- 4.1. INTRODUCTION 26 -- 4.2. METHODS 26 -- 4.3. GENERAL DESCRIPTION OF THE MENTAL HEALTHCARE SYSTEMS OF THE SELECTED COUNTRIES. 29 -- 4.3.1. England 29 -- 4.3.2. France 32 -- 4.3.3. Netherlands 34 -- 4.3.4. Spain 36 -- 4.3.5. Canada 38 -- 4.4. SERVICE CASE STUDIES 40 -- 4.4.1. Description of the case studies 40 -- 4.4.2. Barriers to implementation of specific services of MHC 50 -- 4.5. TRANSVERSAL ANALYSIS 52 -- 4.5.1. Approaches to mental health care and mental health services for elderly 52 -- 4.5.2. Information systems 56 -- 4.5.3. The use of performance indicators 57 -- 4.5.4. The provision of funding 57 -- 4.6. LIMITATIONS 58 -- 4.7. CONCLUSIONS 59 -- 5. NOMENCLATURE CODES 60 -- 6. ONLINE SURVEY TO INNOVATIVE INITIATIVES 65 -- 6.1. DUTCH VERSION 65 -- 6.2. FRENCH VERSION 68 -- 7. RECOMMENDATION BUILDING PROCESS 71 -- 7.1. FIRST DRAFT BUILDING OF RECOMMENDATION BASED ON KEY FINDINGS 71 -- 7.2. FINAL WORDING OF RECOMMENDATION AFTER EXPERT MEETING, VALIDATION MEETING AND INTERNAL REVIEW 72
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".