Closing the Gap: From Research to Practice in Mental Health Interventions
Bibliographic record
Abstract
Evidence-based practice is critical but challenging in mental health. Rigorous research-proven interventions often do not yield expected results in the clinical practice. This study aimed to explore factors contributing to the effectiveness of Occupational Connections (OC)-an intervention for promotion of engagement in meaningful occupations in serious mental illness (SMI)-based on case series study of three quasi-experimental studies. The studies focused on people with SMI, admitted to intensive mental health services participated in the OC, as well as on a control condition group. Similar evaluation procedures throughout these studies addressed primary outcomes of participation dimensions and recovery orientation, as well as secondary outcomes of functional capacity, cognition, and symptom severity. Patterns of changes in outcome measures varied between the three studies as to direction and extension. In the OC groups, 29-60% of the outcome measurements were changed, in comparison to 29-43% of measurements in the control groups. The secondary outcomes were consistently improved in the OC (18-100% of measurements) in comparison to the control (18-67%). The analysis of the studies revealed that clinical effectiveness of participation-oriented intervention varied dependent on interplay between the clinical context, clinician actions, served persons' characteristics, and evidence-building process. These factors should be considered to maximize research benefits for practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.498 | 0.644 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.032 | 0.040 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".