Local Health Department Engagement in Access to Mental Health Services and Mental Health Policy or Advocacy Activities
Bibliographic record
Abstract
CONTEXT: Mental health is a public health concern that requires national attention at the local level. Major issues facing local health departments (LHDs) to actively engage in mental health activities include inadequate surveillance data and limited resources. OBJECTIVE: To examine the levels of engagement in access to mental health services, as well as policy or advocacy activities, by LHDs characteristics. DESIGN: The study design is cross-sectional based on the national survey of LHDs. We analyzed the survey data from the National Association of County and City Health Officials' 2019 Profile study. Logistic regression was performed with 6 levels of engagement in mental health activities as the outcome measures. RESULTS: LHDs reported that a majority had assessed the gaps in access to mental health (57.69%), followed by other activities-had implemented strategies to increase access to mental health (48.77%), implemented strategies to target underserved populations (40.66%), evaluated strategies to target underserved populations (38.84%), engaged in policy/advocacy to address mental health (32.27%), and finally addressed gaps through provision of mental health (22.31%). LHDs' governance structure was strongly associated with engagement in all 6 mental health activities. LHDs that had performed improvement processes, had behavioral health staff, and had cross-jurisdictional sharing were more likely to be engaged in all 6 of the mental health activities. LHDs were also more likely to be engaged in 5 of the 6 mental health activities if they had some relationships with faith communities and in 4 of the 6 mental health activities if they had some relationships with community health centers. CONCLUSIONS: Levels of engagement in mental health policy or advocacy activities among LHDs were low and varied by LHD characteristics. Intervention strategies may include encouraging LHDs to actively engage in mental health activities, participating in Public Health Accreditation Board accreditation program, and incentivizing mental health workforce retention.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".