Structure and Functioning of Acute Inpatient Psychiatric Units in Spain: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: As a consequence of the decentralization of health care provision to the different Regions (called Autonomous Communities) in Spain, different health care models and resources have been developed for psychiatric patients. It would be very useful to obtain comprehensive and comparative data on health care models, resources, and activity of acute inpatient psychiatric units (AIPUs) as a key part of mental health systems. OBJECTIVE: The aim of this study was to determine the current state of AIPUs in Spain through a national scorecard that allows the current situation to be visualized in terms of resources, processes, and outputs. METHODS: A 104-item online questionnaire was sent to all the AIPUs of the different Regions in Spain. It was divided into 11 sections, including data on the resources, processes, and outputs of the AIPUs plus general data, an indicator dashboard, and good practices. RESULTS: The questionnaire was completed by 60.0% (117/195) of the AIPUs invited to participate. The information collected has allowed us to obtain a detailed snapshot of the current situation of AIPUs in Spain at the levels of infrastructure and material resources, staffing, organization and activity of the units, coordination with other units, guidelines, processes and protocols used, participation and communication with patients and their families, teaching activity, and research linked to the units. CONCLUSIONS: This project aimed to help understand the general situation of AIPUs in Spain and its different Regions, contribute to enhancing the benchmarking and harmonization among Spanish Regions, and provide data for future comparisons with other countries. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/26214.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".