The Self‐Assessment for Organizational Capacity Instrument for Evidence‐Informed Health Policy: Preliminary Reliability and Validity of an Instrument
Bibliographic record
Abstract
BACKGROUND: Health policymakers work in organizations that involve multiple competing demands and limited time to make decisions. Influential international policy organizations continue to publish guidance and recommendations without the use of high-quality research evidence. Few studies have described the process with which governments, including health ministries, use evidence to support health policymaking decisions. Research is needed to better understand the psychometric properties of instruments that assess health policy organizations' capacity to use research evidence. AIM: The purpose of this study was to assess the preliminary psychometric properties of an instrument which assesses organizational capacity for evidence use. METHODS: The instrument was administered by telephone survey from January to June 2011 using a purposeful sample of 57 Canadian health policymakers (policy analyst and senior management levels). Reliability of the instrument was assessed with Cronbach's α coefficient and item-to-total correlation for internal consistency; interitem coefficients were used to identify particular item redundancy. Discriminant validity was assessed using the known-group comparison approach, with the independent sample t-test to assess subscale responses of policy analysts and senior managers. FINDINGS: Cronbach's α indicated acceptable internal consistency across its subscales. Discriminant validity analysis revealed a statistically significant difference between policy analysts and senior managers for one subscale. LINKING EVIDENCE TO ACTION: Our study provides a first look at the Self-assessment for Organizational Capacity Instrument's psychometric properties and demonstrates that this instrument can be useful when evaluating government and other organizations' use of evidence to inform decision making. Further testing of this instrument is needed using large and varied samples of policymakers, from policy analysts to senior managers, across varied policymaking contexts. This instrument can be a starting point for government and related organizations to better understand how well it supports evidence use, including its acquisition, appraisal, and use in health policy decision making.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".