The Hidden Complexity of Long-Term Care: How Context Mediates Knowledge Translation and Use of Best Practices
Bibliographic record
Abstract
PURPOSE: Context is increasingly recognized as a key factor to be considered when addressing healthcare practice. This study describes features of context as they pertain to knowledge use in long-term care (LTC). DESIGN AND METHODS: As one component of the research program Translating Research in Elder Care, an in-depth qualitative case study was conducted to examine the research question "How does organizational context mediate the use of knowledge in practice in long-term care facilities?" A representative facility was chosen from the province of Saskatchewan, Canada. Data included document review, direct observation of daily care practices, and interviews with direct care, allied provider, and administrative staff. RESULTS: The Hidden Complexity of Long-Term Care model consists of 8 categories that enmesh to create a context within which knowledge exchange and best practice are executed. These categories range from the most easily identifiable to the least observable: physical environment, resources, ambiguity, flux, relationships, and philosophies. Two categories (experience and confidence, leadership and mentoring) mediate the impact of other contextual factors. Inappropriate physical environments, inadequate resources, ambiguous situations, continual change, multiple relationships, and contradictory philosophies make for a complicated context that impacts care provision. IMPLICATIONS: A hidden complexity underlays healthcare practices in LTC and each care provider must negotiate this complexity when providing care. Attending to this complexity in which care decisions are made will lead to improvements in knowledge exchange mechanisms and best practice uptake in LTC settings.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.015 |
| 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".