P05.55. Qualitative findings from piloting the LEAP Project – an online spirituality-based depression intervention for young adults
Bibliographic record
Abstract
Participants were recruited using a convenience sampling strategy and invited to partake in an in-depth, semi-structured interview upon completion of the intervention. The LEAP Project intervention consists of eight modules (1) Consciousness and Self Acceptance, (2) Appreciation of Beauty & Creativity, (3). Mystery and Meaning, (4) Gratitude, (5) Compassion, (6) Acceptance, (7) Forgiveness, and (8) Celebration. Each is presented using video clips, music, visualizations, true stories and life practices. All interviews were digitally recorded and transcribed verbatim. Data were analyzed using a descriptive qualitative content analysis approach. Transcripts were read and re-read, and coded to preliminary categories and emerging themes of substantive meanings through an iterative process. The impact of the intervention on participants was multi-faceted. Participants reflected that the intervention was grounding and led to changed perspectives of others, themselves, and their surroundings. Participants experienced a greater sense of control over emotions and resulting behaviors, as well as a sense of connection and realization that they are not alone. The latter was identified as a key element in their ability to cope with depression. Participants described an improvement in well-being characterized by: higher energy levels, increased motivation, reduced negative feelings such as fear, anger, frustration, and reduced feelings of depression. Our findings suggest that the LEAP Project impacts young adults with depression by shifting perspectives of life situations and others, providing a sense of control and connection, improving coping abilities and enhancing well-being. Such a program may be a useful and valuable resource in the treatment of depression.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".