Navigating pathways to care: exploring older adults’ experiences seeking psychological care using the Network Episode Model-II
Bibliographic record
Abstract
Within the next 20 years, mental health problems are projected to be the leading cause of disability in Canada. Given that one in four Canadians is expected to be over the age of 65 by 2036, older adults’ mental health problems are a growing public health concern, especially because the rate of mental health service use is particularly low among this age group. Although there have been several decades of mental health research, it is still not well understood why older adults are not accessing treatment. A possible explanation for this is that identifying reasons for poor access alone does not capture the multifaceted, complex nature of individuals’ experiences with mental health problems and their paths into treatment. To address this gap in the literature, this study explored older adults’ experiences seeking psychological care and the factors that influence this dynamic process using the Network Episode Model-II (NEM-II; Pescosolido & Boyer, 2010; Pescosolido et al., 2013). To achieve this objective I conducted 15 individual semi-structured interviews with adults 60 years of age and older, who were receiving outpatient psychological services from a hospital in Winnipeg, Manitoba. Findings from this study suggest that participants’ background (social content), social support network, and the treatment system influence, and are influenced by, participants’ illness careers. Factors that delayed participants’ help-seeking included: a lack of support, “inappropriate” referrals or advice from treatment professionals, and a lack of knowledge among older adults about mental health and treatment options. This research has implications for researchers, clinicians, and public policy initiatives aimed at enhancing older adults’ access to psychological care.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".