Interventions that support major life transitions in older adulthood: a systematic review
Bibliographic record
Abstract
ABSTRACTBackground:Major life transitions can negatively impact the emotional well-being of older people. This study examined the effectiveness of interventions that target the three most common transitions in later life, namely bereavement, retirement, and relocation. METHODS: A systematic search was performed via MEDLINE, EMBASE, CINAHL, Cochrane Library, PsycINFO, and reference lists of retrieved non-randomized and randomized controlled trials (RCTs) in English that studied the effectiveness of interventions addressing the three transitions in those >50 years of age. Two researchers independently selected the publications, piloted the data extraction form, and critically appraised studies specific to transition type and study design. RESULTS: A total of 11 studies (bereavement: 7; retirement: 2; relocation: 2) of 8 unique interventions met the inclusion criteria of which nine were RCTs and two were of quasi-experimental designs were reviewed. Six studies were group-based interventions, three studies used individualized sessions, and one intervention used a combination of group and individualized programming. Group size varied (20-32 participants), as did qualifications of those administering the interventions. The methodological quality of included studies was weak. Findings suggest that group-based approaches provided by trained personnel can mitigate the negative health-related consequences associated with major transitions in later life. CONCLUSION: Evidence concerning interventions that address mental health challenges associated with these major transitions is limited. Future research should better characterize participants at study outset and use validated measures to capture effectiveness. Use of peer mentorship to navigate such transitions is promising, but given the small number of studies and their methodological weaknesses, further research on effectiveness is warranted.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".