A systematic review of the psychometric properties of self-report research utilization measures used in healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In healthcare, a gap exists between what is known from research and what is practiced. Understanding this gap depends upon our ability to robustly measure research utilization. OBJECTIVES: The objectives of this systematic review were: to identify self-report measures of research utilization used in healthcare, and to assess the psychometric properties (acceptability, reliability, and validity) of these measures. METHODS: We conducted a systematic review of literature reporting use or development of self-report research utilization measures. Our search included: multiple databases, ancestry searches, and a hand search. Acceptability was assessed by examining time to complete the measure and missing data rates. Our approach to reliability and validity assessment followed that outlined in the Standards for Educational and Psychological Testing. RESULTS: Of 42,770 titles screened, 97 original studies (108 articles) were included in this review. The 97 studies reported on the use or development of 60 unique self-report research utilization measures. Seven of the measures were assessed in more than one study. Study samples consisted of healthcare providers (92 studies) and healthcare decision makers (5 studies). No studies reported data on acceptability of the measures. Reliability was reported in 32 (33%) of the studies, representing 13 of the 60 measures. Internal consistency (Cronbach's Alpha) reliability was reported in 31 studies; values exceeded 0.70 in 29 studies. Test-retest reliability was reported in 3 studies with Pearson's r coefficients > 0.80. No validity information was reported for 12 of the 60 measures. The remaining 48 measures were classified into a three-level validity hierarchy according to the number of validity sources reported in 50% or more of the studies using the measure. Level one measures (n = 6) reported evidence from any three (out of four possible) Standards validity sources (which, in the case of single item measures, was all applicable validity sources). Level two measures (n = 16) had evidence from any two validity sources, and level three measures (n = 26) from only one validity source. CONCLUSIONS: This review reveals significant underdevelopment in the measurement of research utilization. Substantial methodological advances with respect to construct clarity, use of research utilization and related theory, use of measurement theory, and psychometric assessment are required. Also needed are improved reporting practices and the adoption of a more contemporary view of validity (i.e., the Standards) in future research utilization measurement studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it