P.007 Tailoring a mindfulness intervention as a therapeutic intervention for mild cognitive impairment
Bibliographic record
Abstract
Background: Non-pharmacological interventions that promote self-management are crucial for individuals with mild cognitive impairment (MCI.) Mindfulness training has shown promise but is often not tailored to MCI. Methods: In 2021, the Neil and Susan Manning Cognitive Health Initiative (CHI) - a collaboration between the Vancouver Island Health Authority, Universities of BC and Victoria, and the Victoria Hospitals Foundation - partnered with the BC Association for Living Mindfully (BCALM) to develop a specialized mindfulness program for MCI, based on Mindfulness-Based Stress Reduction (MBSR). This multi-phase initiative aimed to enhance self-management, address the lack of outpatient services for MCI in Victoria, and contribute to the evidence base for mindfulness interventions. Results: Phase 1 assessed the BCALM program’s suitability for MCI; feedback included suggestions to simplify content and meditations. Phase 2 piloted an adapted version, with an 8-week program consisting of weekly sessions. Participants, recruited from the Seniors Outpatient Clinic in Victoria, completed pre- and post-program surveys; results showed over 90% of participants reported improved memory and coping, and 80% managed memory-related challenges better. Conclusions: Now in Phase 3, the MCI program is being transitioned into regular BCALM curriculum, with plans for a clinical trial comparing it to traditional psychoeducational approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".