Translation and validation of the Alberta Context Tool for use in Norwegian nursing homes
Bibliographic record
Abstract
PURPOSE: Organizational context is recognized as important for facilitating evidence-based practice and improving patient outcomes. Organizational context is a complex construct to measure and appropriate instruments that can quantify and measure context are needed. The aim of this study was to translate and cross-culturally adapt the Alberta Context Tool (ACT) to Norwegian, and to test the reliability and structural validity among registered nurses (RNs) and licenced practice nurses (LPNs) working in nursing homes. METHODS: This study was a validation study utilizing a cross-sectional design. The sample consisted of n = 956 healthcare personnel from 28 nursing homes from a municipality in Norway. In the first stage, the ACT was translated before being administered in 28 nursing homes. In the second stage, internal consistency and structural validity were explored using Cronbach's alpha and confirmatory factor analysis. RESULTS: A rigorous forward-and-back translation process was performed including a team of academics, experts, professional translators and the copyright holders, before an acceptable version of the ACT was piloted and finalized. The Norwegian version of the ACT showed good internal consistency with Chronbachs alpha above .75 for all concepts except for Formal interactions where the alpha was .69. Structural validity was acceptable for both RNs and LPNs with factors loadings more than .4 for most items. CONCLUSIONS: The Norwegian version of the ACT is a valid measure of organizational context in Norwegian nursing homes among RNs and LPNs.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".