Development and content validation of a measure to assess evidence-informed decision-making competence in public health nursing
Bibliographic record
Abstract
There are professional expectations for public health nurses to develop competencies in evidence-informed decision-making (EIDM) due to its potential for improved client outcomes. Robust tools to assess EIDM competence can encourage increased EIDM engagement and uptake. This study aimed to develop and validate the content of a measure to assess EIDM competence among public health nurses. A four-stage process, based on measure development principles and the Standards for Educational and Psychological Testing, was used to develop and refine items for a new EIDM competence measure: a) content coverage assessment of existing measures; b) identification of existing measures for use and development of items; c) validity assessment based on content; d) validity assessment based on response process. An EIDM competence measurement tool consisting of EIDM knowledge, skills, attitudes/beliefs, and behaviour items was developed using conceptual literature and existing measures (Evidence-Based Practice Competency Tool and Evidence-Based Practice Beliefs Scale) to address limitations of existing EIDM tools identified from the content coverage assessment. Item content validity index ratings ranged from 0.64-1.00. Qualitative themes from validity assessment based on content and response process included word changes to improve clarity, reducing item redundancy, separating multi-component items, and ensuring items reflect nursing role expectations. Upon determining its reliability and validity, there is potential for the EIDM competence measure to be used in: public health nursing practice to identify competence gaps and strengths to facilitate professional development activities; in research to support development of strategies to build EIDM capacity; and for curriculum planning and development across nursing education programs.
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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 | 0.079 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".