Mental health knowledge and training needs among direct care workers: a mixed methods study
Bibliographic record
Abstract
OBJECTIVES: Direct care providers (DCWs) spend the most time with clients in the home, and as such, play an integral role in identifying mental health problems. However, DCWs receive little preparation in mental health and there is little research regarding their role in the mental health care of clients. The purpose of this study was to explore DCWs' knowledge, attitudes, and experiences of caring for clients with mental health problems from the perspectives of DCWs and key administrators (KAs). METHOD: Mixed method design. Structured interviews were conducted with DCWs. Focus groups were conducted with KAs. RESULTS: Twenty-nine DCWs and 12 KAs took part in the study. Loneliness and memory problems in clients were the most prevalent challenges identified by DCWs. DCWs' self-reported mental health knowledge was mid to high across all domains, although they had many misconceptions about mental health and aging. Helpful strategies in working with clients included communication skills, rapport-building, behavioral, cognitive, emotion-regulation, and making use of external resources. KAs noted individual differences in DCWs' mental health knowledge and indicated that mental health issues were often viewed by DCWs as dispositional problems or a normal part of aging. KAs viewed DCWs' greatest challenges as personalizing difficult client behaviors, lack of knowledge about how to manage specific behaviors, and difficulties managing their own emotions towards clients. CONCLUSION: Data from this study suggest important areas for DCW development. However, system issues that affect DCWs such as workload, resources, mental health stigma, and diverse client populations should be addressed concurrently.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".