<i>“It's Hard to Give Hope Sometimes”:</i> Climate Change, Mental Health, and the Challenges for Mental Health Professionals
Bibliographic record
Abstract
Mental health professionals (MHPs) are on the frontlines of assisting clients with mental health impacts of climate change (CC), yet challenges to their practice and required resources have not been adequately explored. A cross-sectional online knowledge, attitudes and practice (KAP) survey was conducted with active, licensed MHPs across the State of Minnesota ( n = 517). Fifty-four questions were divided into sections on socio-demographics, knowledge and attitudes, familiarity with emerging terminology, practice behaviors and experiences, and needs for professional resources and training. Most MHPs agreed that CC is an important problem impacting mental health (81.6%), with many (61.0%) already observing these impacts. More than half (51.8%) report that clients would consider discussing CC as part of their treatment. Yet fewer (32.9%) feel well-prepared to have this discussion. A small proportion of MHPs are familiar with resources to assist with assessment (15.0%) and treatment (18.3%), but only 10.2% have made use of these tools with their clients. Results from this comprehensive survey underscore the need for interdisciplinary research and practice communities to design and implement assessment, intervention, and evaluation tools that address the broad impacts of CC on help-seeking clients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".