Collaborative sustainability planning for an outcome measure in outpatient stroke rehabilitation: A qualitative description study
Bibliographic record
Abstract
RATIONALE: Evidence suggests that sustainability planning and the use of a collaborative approach to planning result in better sustainability outcomes and more relevant knowledge. Yet, both approaches appear to be underutilized. A detailed description of collaborative sustainability planning may encourage the use of these two impactful strategies. AIMS AND OBJECTIVES: To explore the collaborative sustainability planning process for a single outcome measure in three rehabilitation sites. METHODOLOGY: Within the Mayo-Portland Adaptability Inventory-version 4 (MPAI-4) implementation project, we conducted a qualitative description study. We used data from 12 core sustainability planning meetings and 108 follow-up meetings that included a total of 31 clinical and research team participants. Sustainability planning was informed by an MPAI-4-specific implementation guide, and by the results from a realist review of the sustainability of rehabilitation practices and the Clinical Sustainability Assessment Tool. We analyzed qualitative data using thematic content analysis. RESULTS: Three themes describe the collaborative sustainability planning process: (1) "collaboration as a driver for sustainability" which captures the active collaboration underpinning sustainability planning; (2) "co-creation of a sustainability plan to achieve shared objectives" which captures the identified barriers and facilitators, and selected sustainability strategies linked to one of six collaboratively identified shared objectives; and (3) "the iterative nature of sustainability planning" which captures the necessity of an agile and responsive sustainability planning process. CONCLUSION: Identified strategies may be useful to support (collaborative) sustainment. Future research could investigate the effect of collaborative sustainability planning on sustainability objectives, and the relationship between these objectives.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".