Effectiveness of capacity building interventions relevant to public health practice: a systematic review
Bibliographic record
Abstract
BACKGROUND: This systematic review assessed the effectiveness of capacity building interventions relevant to public health practice. The aim is to inform and improve capacity building interventions. METHODS: Four strategies were used: 1) electronic database searching; 2) reference lists of included papers; 3) key informant consultation; and 4) grey literature searching. Inclusion (e.g., published in English) and exclusion criteria (e.g., non-English language papers published earlier than 2005) are outlined with included papers focusing on capacity building, learning plans, or professional development plans within public health and related settings, such as non-governmental organizations, government, or community-based organizations relating to public health or healthcare. Outcomes of interest included changes in knowledge, skill or confidence (self-efficacy), changes in practice (application or intent), and perceived support or supportive environments, with outcomes reported at the individual, organizational or systems level(s). Quality assessment of all included papers was completed. RESULTS: Fourteen papers were included in this review. These papers reported on six intervention types: 1) internet-based instruction, 2) training and workshops, 3) technical assistance, 4) education using self-directed learning, 5) communities of practice, and 6) multi-strategy interventions. The available literature showed improvements in one or more capacity-building outcomes of interest, mainly in terms of individual-level outcomes. The available literature was moderate in quality and showed a range of methodological issues. CONCLUSIONS: There is evidence to inform capacity building programming and how interventions can be selected to optimize impact. Organizations should carefully consider methods for analysis of capacity building interventions offered; specifically, through which mechanisms, to whom, and for which purpose. Capacity-building interventions can enhance knowledge, skill, self-efficacy (including confidence), changes in practice or policies, behaviour change, application, and system-level capacity. However in applying available evidence, organizations should consider the outcomes of highest priority, selecting intervention(s) effective for the outcome(s) of interest. Examples are given for selecting intervention(s) to match priorities and context, knowing effectiveness evidence is only one consideration in decision making. Future evaluations should: extend beyond the individual level, assess outcomes at organizational and systems levels, include objective measures of effect, assess baseline conditions, and evaluate features most critical to the success of interventions.
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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 | 0.041 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".