How Does the Facilitation Effort of Clinical Educators Interact With Aspects of Organizational Context to Affect Research Use in Long‐term Care? Evidence From CHAID Analysis
Bibliographic record
Abstract
PURPOSE: Organizational context influences the effect of facilitation efforts on research use in care settings. The interactions of these factors are complex. Therefore, the use of traditional statistical methods to examine their interrelationships is often impractical. Big Data analytics can automatically detect patterns within the data. We applied the chi-squared automatic interaction detection (CHAID) algorithm and classification tree technique to explore the dynamic and interdependent relationships between the implementation science concepts-context, facilitation, and research use. DESIGN: Observational, cross-sectional study based on survey data collected from a representative sample of nursing homes in western Canada. METHODS: We assessed three major constructs: (a) Conceptual research utilization (CRU) using the CRU scale; (b) facilitation of research use measured by the frequency of contacts between the frontline staff and a clinical educator, or person who brings new ideas to the care unit; and (c) organizational context at the unit level using the Alberta Context Tool (ACT). CHAID analysis was performed to detect the interactions between facilitation and context variables. Results were illustrated in a classification tree to provide a straightforward visualization. FINDINGS: Data from 312 care units in three provinces were included in the final analysis. Results indicate significant multiway interactions between facilitation and various aspects of the organizational context, including leadership, culture, evaluation, structural resources, and organizational slack (staffing). Findings suggested the preconditions of the care settings where research use can be maximized. CONCLUSIONS: CHAID analysis helped transform data into usable knowledge. Our findings provide insight into the dynamic relationships of facilitators' efforts and organizational context, and how these factors' interplay and their interdependence together may influence research use. CLINICAL RELEVANCE: Knowledge of the combined effects of facilitators' efforts and various aspects of organizational context on research use can contribute to effective strategies to narrow the evidence-practice gap in care 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 | 0.105 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".