‘Mental health is a mystery’: patient perspectives on treatment engagement in the referral process to specialty geriatric mental health services
Bibliographic record
Abstract
OBJECTIVES: Raue and Sirey proposed a theoretical treatment engagement model for older adults outlining steps from identifying mental health problems, referral to specialty care, and involvement in treatment. Using this model as a guide, the current study aimed to explore patient perspectives of their experience in the process of referral and first meeting with geriatric mental health services. Furthermore, the current study aimed to identify opportunities to enhance patient engagement in these beginning steps of the treatment engagement process. METHOD: Thirteen geriatric outpatients (7 psychology, 6 psychiatry) presenting with concerns of anxiety, depression, and/or stress were interviewed. Interviews were analyzed using the framework method. RESULTS: Themes emerged as suggested by Raue and Sirey's model, including attitudes toward treatment (e.g. stigma), treatment expectations, and treatment preferences. In addition, new themes emerged related to modifiable individual factors (the patient as a passive recipient of care, mental health literacy, and ageism) as well as social influences on treatment engagement. Participants primarily noted opportunities for psychoeducation as a potential treatment engagement intervention to implement within the current referral system. CONCLUSIONS: This is the first study to examine the applicability of Raue and Sirey's theoretical engagement model in a clinical sample. Findings both support and expand the model and offer several recommendations for improving treatment engagement for older patients.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".