LEAVES (optimizing the mentaL health and resiliencE of older Adults that haVe lost thEir spouSe via blended, online therapy): Proposal for an Online Service Development and Evaluation
Bibliographic record
Abstract
BACKGROUND: Loss of a spouse is a frequent occurrence in later life. While most older adults successfully process this loss and will return to a normal life, about 10% of the individuals are unable to cope, and progress to prolonged grief (PG). PG, in turn, can result in mental and physical problems including poor sleep, cardiovascular problems, depression, and suicidal tendencies. OBJECTIVE: LEAVES (optimizing the mentaL health and resiliencE of older Adults that haVe lost thEir spouSe via blended, online therapy) is an online bereavement program that will support the prevention and treatment of PG, so that elderly mourners can continue to lead an active, meaningful, and dignified life. LEAVES will cater to secondary end users (eg, family, informal caregivers) by reducing stress. METHODS: LEAVES will help older adults to process the loss of a spouse in an online environment, which consists of (1) an existing online grief self-help program LIVIA, (2) the Before You Leave program that allows for storing personal memories, (3) a virtual agent platform, and (4) an accessible front-end design. LEAVES can detect persons at risk for complications, reveal negative trends in their emotional life, and act to counter such trends. The service relies on online support whenever possible but is blended with telephone or face-to-face counseling when necessary. RESULTS: The project will take place between February 2020 and January 2023 and includes a real-life evaluation in which 315 end users will use the service across 3 countries (the Netherlands, Portugal, and Switzerland). The evaluation of LEAVES will focus on clinical effect, its business case, and technology acceptance. The results will pave the way for smooth integration into existing care paths and reimbursement schemes. CONCLUSIONS: The LEAVES service aims to soften the mourning process, prevents depression or social isolation, strengthens widow(er)s resilience and well-being, and quickens one's return to societal participation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19344.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".