The Effectiveness of Public Awareness Initiatives Aimed at Encouraging the Use of Evidence-Based Recommendations by Health Professionals: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Public awareness initiatives have attracted growing attention globally, as a strategy to reduce low-value care and disinformation. However, knowledge gap remains in determining their effects. The aim of this systematic review was to summarize existing evidence to date on global effectiveness of public awareness initiatives. METHODS: Primary quantitative studies focusing on passive delivery of public awareness initiatives that targeted health professionals were included. Eligible studies were identified through search of MEDLINE, Embase, Emcare, the Cochrane Library, PsycINFO, Business Source Complete, Emerald Insight, and Google (initially on December 19, 2018, followed by updated search between July 8-10, 2019, and then between March 8-9, 2022) and the reference list of relevant studies. Methodological quality of included studies was assessed using modified McMaster critical appraisal tool. A narrative synthesis of the study outcomes was conducted. RESULTS: Twenty studies from United States, United Kingdom, Canada, Australia, and multicountry were included. Nineteen studies focused on Choosing Wisely initiative and one focused on National Institute of Clinical Excellence reminders. Most studies investigated one recommendation of a specialty. The findings showed conflicting evidence on the effectiveness of public awareness initiatives, suggesting passive delivery has limited success in reducing low-value care among health professionals. CONCLUSIONS: This review highlights the complexity of change in an established practice pattern in health care. As passive delivery of public awareness initiatives has limited potential to initiate and sustain change, wide-ranging intervention components need to be integrated for a successful implementation.
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.
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 | 0.029 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".