Healthcare professionals' perspectives on patient mental health treatment engagement in later life
Bibliographic record
Abstract
Older adults face complex challenges in seeking and receiving mental health treatment. While older adults access psychological services less frequently than other populations, strong engagement can predict better treatment outcomes. This study explores practitioners’ perspectives on the treatment engagement of patients referred for specialty geriatric mental health services. Guided by Raue & Sirey’s (2011) late-life treatment engagement model, 11 interviews were conducted with specialty geriatric mental health professionals (geriatric psychiatrists and gero-psychologists) and frequent referral sources such as general practitioners and other specialty health providers to assess their perspectives on patient treatment engagement barriers and potential improvement to the current referral process. The data were analyzed using the framework analytic approach, and summative content analysis was used to extract suggested treatment engagement interventions. Results from the framework analysis highlighted mental health literacy, attitudes, and beliefs as key influences on treatment decisions and the importance of patient-centered care, strong relationship dynamics, and collaboration among providers and older adults when navigating treatment systems. Streamlined communication between the referral source, specialists, and patients was also proposed with a call to educate family doctors, and patients, and their families about available services, referral processes, and how to openly discuss mental health. Findings from this study highlight the importance of considering provider and referrer factors in attempts to better engage patients and suggest a need for interventions to improve knowledge about mental health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".