Older adults' pathways to mental health information and treatment: Bridging the gap in knowledge translation
Bibliographic record
Abstract
A growing body of research has emphasized the prevalent mental health problems faced by the fastest-growing demographic segment of Canada’s population, older adults, in addition to their particularly low rates of mental health service use. Research has also begun to demonstrate that although older adults express a desire to be involved in their health care decision-making, they are often not given sufficient information to participate in this process. In light of low rates of service use and generally poor mental health literacy, defined as knowledge and beliefs about the recognition, prevention, and management of mental health problems, several researchers posit that older adults experience a gap in the knowledge translation of mental health information. The present research explores older adults’ pathways to mental health information and treatment. In Study 1, individual interviews were conducted with older adults who came to seek psychological treatment for mental health problems (n = 15), and analyzed according to narrative analysis. The main storylines across participants’ narratives of treatment seeking included resistance to being labeled with mental health problems, muddling through the treatment seeking process, and interpretations of psychological treatment. Findings are discussed within the context of increasing efforts to enhance clarity in the complex process of seeking treatment for mental health problems. In Study 2, older adults’ mental health information preferences and predictors of information preferences were examined in a sample of community-dwelling older adults (n = 229). Results demonstrated that despite being unfamiliar with mental health treatment options, older adults reported a strong interest in receiving detailed information concerning a variety of mental health treatment options. Family, friends, and health care providers were highly rated informational sources; and written formats and discussions with health care providers were highly rated informational formats. The most consistent predictors of mental health information preferences included attitudes toward seeking psychological treatment and social support. Findings are contextualized within the importance of increasing the mental health literacy of older adults through knowledge translation efforts. Overall, findings of this research provide clear directions for decreasing the gap in mental health knowledge translation among older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".