Mapping barriers and intervention activities to behaviour change theory for Mobilization of Vulnerable Elders in Ontario (MOVE ON), a multi-site implementation intervention in acute care hospitals
Bibliographic record
Abstract
BACKGROUND: As evidence-informed implementation interventions spread, they need to be tailored to address the unique needs of each setting, and this process should be well documented to facilitate replication. To facilitate the spread of the Mobilization of Vulnerable Elders in Ontario (MOVE ON) intervention, the aim of the current study is to develop a mapping guide that links identified barriers and intervention activities to behaviour change theory. METHODS: Focus groups were conducted with front line health-care professionals to identify perceived barriers to implementation of an early mobilization intervention targeted to hospitalized older adults. Participating units then used or adapted intervention activities from an existing menu or developed new activities to facilitate early mobilization. A thematic analysis was performed on the focus group data, emphasizing concepts related to barriers to behaviour change. A behaviour change theory, the 'capability, opportunity, motivation-behaviour (COM-B) system', was used as a taxonomy to map the identified barriers to their root causes. We also mapped the behaviour constructs and intervention activities to overcome these. RESULTS: A total of 46 focus groups were conducted across 26 hospital inpatient units in Ontario, Canada, with 261 participants. The barriers were conceptualized at three levels: health-care provider (HCP), patient, and unit. Commonly mentioned barriers were time constraints and workload (HCP), patient clinical acuity and their perceived 'sick role' (patient), and lack of proper equipment and human resources (unit level). Thirty intervention activities to facilitate early mobilization of older adults were implemented across hospitals; examples of unit-developed intervention activities include the 'mobility clock' communication tool and the use of staff champions. A mapping guide was created with barriers and intervention activities matched though the lens of the COM-B system. CONCLUSIONS: We used a systematic approach to develop a guide, which maps barriers, intervention activities, and behaviour change constructs in order to tailor an implementation intervention to the local context. This approach allows implementers to identify potential strategies to overcome local-level barriers and to document adaptations.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".