Moving Forward: A new internet-delivered program integrating life review therapy and self-compassion may lessen depression and anxiety in people facing life transitions
Bibliographic record
Abstract
Life Review Therapy (LRT) is an evidence-based treatment for depression in the elderly. Some evidence suggests that LRT may be helpful for any individual going through a life transition as people tend to reminisce during such times. Preliminary findings also support the guided online delivery of LRT. The reflective nature of the therapy may however be challenging in the absence of clinical guidance. Self-compassionate writing may facilitate this reflective process. The present study examined the feasibility of a new Internet-delivered LRT based on self-compassion called Moving Forward for the management of life transitions among adults. Twenty participants were included in the analyses. The intervention is a 6-week program including psychoeducation and writing exercises related to reminiscence activities, life review therapy, self-compassion and best possible self. Most participants (71.4%) completed at least four of the six weeks of therapy. An attrition rate of 28.6% was obtained. Acceptability was high with 93.3% of the participants who reported that the program was worth their time. Mixed effect models analyses revealed significant and large pre-post treatment reductions in depression. Gains were maintained at a 3-month follow-up. Overall, the results support the feasibility of the Moving Forward program. A randomized controlled trial is needed to assess its efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".