Using the Theoretical Domains Framework to Identify Barriers and Facilitators to Elder‐Friendly Care Implementation Within a Multi‐Site Academic Health Centre
Bibliographic record
Abstract
BACKGROUND: Societal demographic shifts are occurring globally. Within Quebec, Canada, the percentage of adults over 65 (older adults) is predicted to increase from 19.3% to >25.9% by the year 2036. Older adults (OAs) experience hospitalizations more frequently than persons aged 15-64 years old, and hospitalizations for OAs can be detrimental due to naturally occurring physiological changes. To address the needs of this population, the Quebec government mandated that all acute care hospitals implement OA-friendly care standards called AAPA ("l'Approche Adaptée à la Personne Âgée"). AIMS: To describe an approach for identifying barriers and facilitators (BFs) to AAPA implementation at the McGill University Health Centre, an academic healthcare centre in Montreal that provides tertiary and quaternary care. METHODS: Our approach included an organizational quality improvement (QI) model based on the Institute for Healthcare Improvement QI approach and the use of the Theoretical Domains Framework (TDF) to guide the assessment of BFs to AAPA implementation. To identify the BFs of AAPA implementation, themes were generated from the raw data. RESULTS: In total, 32 barriers and 88 facilitators were identified. Each BF was linked to one or more corresponding domain from the TDF. Seven of the most frequently occurring domains were: (1) knowledge, (2) beliefs about consequences, (3) social/professional role and identity, (4) social influences, (5) environmental context and resources, (6) intentions, and (7) goals. LINKING EVIDENCE TO ACTION: A theory-informed approach, such as the TDF, can be used to facilitate the implementation of evidence-based guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".