COMPLEXITY IN PSYCHOSOCIAL INTERVENTIONS: CASE STUDIES FROM A STROKE TRANSITIONS TRIAL
Bibliographic record
Abstract
Abstract The Michigan Stroke Transitions Trial (MISTT) tested whether in-home social work case management (SWCM) or SWCM combined with access to a website providing stoke-related information improved outcomes relative to usual care for patients discharged home post-stroke and their caregivers. The aims of this secondary analysis are 1) to describe the actual support social work case managers (SWCM) provided to MISTT participants and 2) use select case studies to illustrate the relationship between SWCM and quantitative patient and caregiver outcomes. Data for the study were derived from SWCM case notes on 157 patients and their caregivers who received the MISTT intervention. Case notes were coded in two steps with a subset of cases coded by two researchers and reviewed for interrater reliability in each step. The first round of coding was guided by primary SWCM intervention goals. The second round of coding identified SWCM sub-themes within each primary goal. Key themes indicate SWCMs aided with understanding the post-hospitalization period, helped patients navigate a range of systems and services, identified needs and supported patient goals, provided psychosocial support, and centered support on stroke recovery and prevention. Case studies illustrate ways in which SWCM were key supports during the transition period, but that support does not cleanly align with quantitative findings from patient-reported outcomes. This study aligns with a growing body of work documenting the complexity of transitions of care and has implications for how we support patients and caregivers as they move from inpatient to outpatient care and measure outcomes.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".