Building readiness in community-based organisations to enable the implementation of public health interventions for adults and older adults: a scoping review
Bibliographic record
Abstract
BACKGROUND: A key challenge to implementing and scaling up evidence-based interventions (EBIs) into practice is organisational readiness; described as an organisation's motivation, general capacities, and capabilities specific to the EBI. Building organisational readiness has been investigated in some health disciplines (e.g., mental health). However, the importance of building organisational readiness to effectively implement public health EBIs for adults and older adults in the community setting remains largely unexplored. Our aim was to examine how readiness was defined and measured, what strategies were used to build readiness, and the relationship between readiness-building strategies and implementation, service-level, and person-level outcomes. METHODS: In this scoping review, we searched seven databases and conducted forward and backward citation tracking. From a pool of eight reviewers, combinations of two reviewers independently screened references for eligibility. A single reviewer extracted data, and a second reviewer checked data. Results for each implementation, service-level and person-level outcome in each study were extracted and categorised as favourable, nonsignificant, or unfavourable. RESULTS: Twelve studies were included, which implemented a mix of different public health EBIs to almost 40,000 participants (n = 37,883; 54% women) across varied community settings. Only four studies defined readiness; all used different definitions. Five studies used five different instruments to assess readiness, all with poor psychometric properties. All studies used multiple strategies to build readiness (range 4-20 strategies per study), with all using strategies to assess, plan and monitor implementation of the EBI (i.e., 'evaluative and iterative strategies') and strategies to support collaboration between organisations delivering the EBI (i.e., 'develop interest-holder interrelationships'). Three-quarters of the strategies focused on building the organisation's capability to deliver the specific EBI (e.g., assessing readiness, conducting educational meetings) and were delivered by external support teams. Exploring the relationship between readiness-building strategies and study outcomes indicated more favourable than unfavourable outcomes, particularly for implementation and service-level outcomes (38/48; 79% favourable). CONCLUSIONS: Within this limited sample, the use of readiness-building strategies improved the implementation of public health EBIs in community organisations. However, consistency of definitions and terminology and more sophisticated testing of readiness-building strategies will help confirm how best to do this. TRIAL REGISTRATION: Open Science Framework, May 5, 2024.
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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 | 0.048 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".