Organizational Context and Facilitation Interactions on Delirium Risk in Long-Term Care: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: Organizational context (eg, leadership) and facilitation (eg, coaching behaviors) are thought to interact and influence staff best practices in long-term care (LTC), including the management of delirium. Our objective was to assess if organizational context and facilitation-individually, and their interactions-were associated with delirium in LTC. DESIGN: Retrospective cross-sectional analysis of secondary data. SETTING AND PARTICIPANTS: We included 8755 residents from 281 care units in 86 LTC facilities in 3 Canadian provinces. METHODS: Delirium (present/absent) was assessed using the Resident Assessment Instrument-Minimum Data Set 2.0 (RAI-MDS 2.0). The Alberta Context Tool (ACT) measured 10 modifiable features of care unit organizational context. We measured the care unit's total care hours per resident day and the proportion of care hours that care aides contributed (staffing mix). Facilitation included the facility manager's perception of RAI-MDS reports' adequacy and pharmacist availability. We included unit managers' change-oriented organizational citizenship behavior (OCB) and an item reflecting how often care aides recommended policy changes. Associations of organizational context, facilitation, and their interactions with delirium were analyzed using mixed-effects logistic regressions, controlling for covariates. RESULTS: Delirium symptoms were prevalent in 17.4% of residents (n = 1527). Manager-perceived adequacy of RAI-MDS reports was linked to reduced delirium symptoms [odds ratio (OR) = 0.63]. Higher care hours per resident day (OR = 1.2) and an available pharmacist in the facility (OR = 1.5) were associated with increased delirium symptoms. ACT elements showed no direct association with delirium. However, on care units with low social capital scores (context), increased unit managers' OCB decreased delirium symptoms. On care units with high vs low evaluation scores (context), increased staffing mix reduces delirium symptoms more substantially. CONCLUSIONS AND IMPLICATIONS: Unit-level interactions between organizational context and facilitation call for targeted quality improvement interventions based on specific contextual factors, as effectiveness may vary across contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".