Association Between Mental Healthcare Shortages and Impact On Essential Community Resources
Bibliographic record
Abstract
This study examines the association between mental healthcare “shortages” and (1) mental health “holds” in hospitals and (2) emergency first responder services. An analysis of monthly time series data from 2018 through 2022 suggests that changesin the number of mental health shortage areas are positively associated with changes in both the number of mental health holds in hospitals and emergency medical service dispatches. Moreover, while there seems to be a “lagged” association between mental healthcare shortages and mental health holds, both contemporaneous and lagged association was observed between mental health shortages and emergency medical dispatches. Keywords: Mental healthcare shortage, community resources, impact on hospitals, impact on first responders _________________________________________________________________________________ Association entre les pénuries de soins de santé mentale et l'impact sur les ressources communautaires essentielles RésuméCette étude examine l’association entre les « pénuries » de soins de santé mentale et (1) les « blocages » de la santé mentale dans les hôpitaux et (2) les services d’urgence de première intervention. Une analyse des données chronologiques mensuelles de 2018 à 2022 suggère que les changements dans le nombre de zones de pénurie de santé mentale sont positivement associés aux changements à la fois dans le nombre d’hospitalisations pour santé mentale et dans les répartitions des services médicaux d'urgence. De plus, bien qu’il semble y avoir une association « décalée » entre les pénuries de soins de santé mentale et les blocages en matière de santé mentale, une association à la fois contemporaine et décalée a été observée entre les pénuries de santé mentale et les envois médicaux d’urgence. Mots clés : Pénurie de soins de santé mentale, ressources communautaires, impact sur les hôpitaux, impact sur les premiers intervenants
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".