Applying modern measurement approaches to constructs relevant to evidence-based practice among Canadian physical and occupational therapists
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is a complex process. To quantify it, one has to also consider individual and contextual factors using multiple measures. Modern measurement approaches are available to optimize the measurement of complex constructs. This study aimed to develop a robust measurement approach for constructs around EBP including practice, individual (e.g. knowledge, attitudes, confidence, behaviours), and contextual factors (e.g. resources). METHODS: One hundred eighty-one items arising from 5 validated EBP measures were subjected to an item analysis. Nominal group technique was used to arrive at a consensus about the content relevance of each item. Baseline questionnaire responses from a longitudinal study of the evolution of EBP in 128 new graduates of Canadian physical and occupational therapy programmes were analysed. Principles of Rasch Measurement Theory were applied to identify challenges with threshold ordering, item and person fit to the Rasch model, unidimensionality, local independence, and differential item functioning (DIF). RESULTS: The nominal group technique identified 70/181 items, and modified Delphi approach identified 68 items that fit a formative model (2 related EBP domains: self-use of EBP (9 items) and EBP activities (7 items)) or a reflective model (4 related EBP domains: attitudes towards EBP (17 items), self-efficacy (9 items), knowledge (11 items) and resources (15 items)). Rasch analysis provided a single score for reflective construct. Among attitudes items, 65% (11/17) fit the Rasch model, item difficulties ranged from - 7.51 to logits (least difficult) to + 5.04 logits (most difficult), and person separation index (PSI) = 0.63. Among self-efficacy items, 89% (8/9) fit the Rasch model, item difficulties ranged from - 3.70 to + 4.91, and PSI = 0.80. Among knowledge items, 82% (9/11) fit the Rasch model, item difficulties ranged from - 7.85 to 4.50, and PSI = 0.81. Among resources items, 87% (13/15) fit the Rasch model, item difficulties ranged from - 3.38 to 2.86, and PSI = 0.86. DIF occurred in 2 constructs: attitudes (1 by profession and 2 by language) and knowledge (1 by language and 2 by profession) arising from poor wording in the original version leading to poor translation. CONCLUSIONS: Rasch Measurement Theory was applied to develop a valid and reliable measure of EBP. Further modifications to the items can be done for subsequent waves of the survey.
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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 | 0.055 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".