The use of technology for mental healthcare delivery among older adults with depressive symptoms: A systematic literature review
Bibliographic record
Abstract
Depression has been identified as the single largest contributor to poor health and functioning worldwide. Global estimates indicate that 4.4% of the world's population lives with depression, equating to about 322 million individuals. Research demonstrates that telehealth interventions (i.e. delivering therapy by phone or videoconferencing) have potential for improving mental health care among community-based older adults. This review analyses scholarly literature on telehealth interventions among older adults with depressive symptoms. Following PRISMA guidelines, a systematic search of peer-reviewed papers was conducted using the following key terms: telemedicine, telepsychogeriatrics, telepsychiatry, eHealth, mental health, depression, and geriatric. The review included nine articles examining telehealth for mental health care, published in English between 1946 and 26 September 2017. Telehealth for mental health care among older adults demonstrates a significant impact on health outcomes, including reduced emergency visits, hospital admissions, and depressive symptoms, as well as improved cognitive functioning. Positive or negative influences on the use of telehealth among older adults are identified. This review highlights keys aspects to consider in using telehealth interventions, including levels of education, cognitive function, and prior technology experience. The review highlights vital factors for designing interventions which aim to capitalize on the benefits of the use of telehealth for mental healthcare service delivery, especially in older adults with depressive symptoms.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".