Older Adult Peer Support Specialists’ Age-Related Contributions to an Integrated Medical and Psychiatric Self-Management Intervention: Qualitative Study of Text Message Exchanges
Bibliographic record
Abstract
BACKGROUND: Middle-aged and older adults with mental health conditions have a high likelihood of experiencing comorbid physical health conditions, premature nursing home admissions, and early death compared with the general population of adults aged 50 years or above. An emerging workforce of peer support specialists aged 50 years or above or "older adult peer support specialists" is increasingly using technology to deliver peer support services to address both the mental health and physical health needs of middle-aged and older adults with a diagnosis of a serious mental illness. OBJECTIVE: This exploratory qualitative study examined older adult peer support specialists' text message exchanges with middle-aged and older adults with a diagnosis of a serious mental illness and their nonmanualized age-related contributions to a standardized integrated medical and psychiatric self-management intervention. METHODS: Older adult peer support specialists exchanged text messages with middle-aged and older adults with a diagnosis of a serious mental illness as part of a 12-week standardized integrated medical and psychiatric self-management smartphone intervention. Text message exchanges between older adult peer support specialists (n=3) and people with serious mental illnesses (n=8) were examined (mean age 68.8 years, SD 4.9 years). A total of 356 text messages were sent between older adult peer support specialists and service users with a diagnosis of a serious mental illness. Older adult peer support specialists sent text messages to older participants' smartphones between 8 AM and 10 PM on weekdays and weekends. RESULTS: Five themes emerged from text message exchanges related to older adult peer support specialists' age-related contributions to integrated self-management, including (1) using technology to simultaneously manage mental health and physical health issues; (2) realizing new coping skills in late life; (3) sharing roles as parents and grandparents; (4) wisdom; and (5) sharing lived experience of difficulties with normal age-related changes (emerging). CONCLUSIONS: Older adult peer support specialists' lived experience of aging successfully with a mental health challenge may offer an age-related form of peer support that may have implications for promoting successful aging in older adults with a serious mental illness.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".