Exploring older adults’ experiences seeking psychological services using the network episode model
Bibliographic record
Abstract
Abstract Older adults’ mental health problems are a growing public health concern, especially because their rate of mental health service use is particularly low. Decades of mental health service utilisation models have been developed, yet key assumptions from these models focus primarily on factors that facilitate or inhibit access into the treatment system without taking into considering the dynamics of how individuals respond to their mental health problems and engage in service utilisation. More recently, dynamic models like the Network Episode Model (NEM-II) have been developed to challenge the underlying, rational choice assumption of traditional utilisation models. Given the multifaceted and complex nature of older adults’ mental health problems, the objective of this study was to examine whether the NEM-II is a helpful and appropriate model for understanding the dynamic process of how older adults navigate the mental health system, including factors that advanced and delayed help-seeking. Our qualitative analyses from 15 interviews with older adults revealed that their backgrounds, social supports and treatment systems influence, and are influenced by, their illness careers. Factors that delayed help-seeking included: a lack of support, ‘inappropriate’ referrals/advice from treatment professionals and poor mental health literacy. This research suggests the NEM-II is a helpful and appropriate theory for understanding older adults’ pathways to treatment, and has implications to enhance older adults’ access to psychological services.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".