Context Matters: Using A Case Study Approach to Understand Implementing A Patient Centred Rehabilitation Model for Persons With Cognitive Impairment
Bibliographic record
Abstract
Abstract Background: There is a growing number of older adults with cognitive impairment (CI) that require inpatient rehabilitation. Patient centred rehabilitation models exist, yet there is a lack of specific strategies for implementing these models into other contexts. Researchers collaborated with administrators and staff in one rural site to adapt a patient centred rehabilitation model of care in the Canadian province of Ontario. This paper reports on the contextual factors that influenced the implementation of the model of care.Methods: The study takes a case study approach. One rural facility was purposefully selected for its interest in offering rehabilitation to persons with CI. Four focus group discussions were conducted to explore healthcare professionals’ perceptions on the contextual factors that could affect the implementation of the rehabilitation model of care in this facility. Twenty-seven professionals with various backgrounds were purposively sampled using a maximum diversity sampling strategy. A hybrid inductive-deductive approach was used to analyze the data using the Context and Implementation of Complex Interventions (CICI) Framework. Results: Across the domains of the CICI framework, three domains (political, epidemiological, and geographical) and seven corresponding sub-domains were found to have a major influence on the implementation process. Key elements within the political domain included effective teamwork, facilitation, adequate resources, effective communication strategies, and a vision for change. Within the epidemiological domain, a key element was knowing how to tailor rehabilitation approaches for persons with CI. Infrastructure was a key aspect of the geographical domain, which was focused on the facility’s physical layout.Conclusions: The study identified key factors within the context that supported and hindered the implementation of the model of care in a new environment. This work suggests that when implementing a new program of care, strong consideration should be paid to the political, epidemiological, and geographical domains of the context and how these aspects interact and influence one another. The CICI Framework was a useful guide to understand which elements existed and which were still required for successful implementation of the model of care.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".