Methods, strategies and technologies used to conduct a scoping literature review of collaboration between primary care and public health
Bibliographic record
Abstract
AIM: This paper describes the methods, strategies and technologies used to conduct a scoping literature review examining primary care (PC) and public health (PH) collaboration. It presents challenges encountered as well as recommendations and 'lessons learned' from conducting the review with a large geographically distributed team comprised of researchers and decision-makers using an integrated knowledge translation approach. BACKGROUND: Scoping studies comprehensively map literature in a specific area guided by general research questions. This methodology is especially useful in researching complex topics. Thus, their popularity is growing. Stakeholder consultations are an important strategy to enhance study results. Therefore, information about how best to involve stakeholders throughout the process is necessary to improve quality and uptake of reviews. METHODS: This review followed Arksey and O'Malley's five stages: identifying research questions; identifying relevant studies; study selection; charting the data; and collating, summarizing and reporting results. Technological tools and strategies included: citation management software (Reference Manager®), qualitative data analysis software (NVivo 8), web conferencing (Elluminate Live!) and a PH portal (eHealthOntario), teleconferences, email and face-to-face meetings. FINDINGS: Of 6125 papers identified, 114 were retained as relevant. Most papers originated in the United Kingdom (38%), the United States (34%) and Canada (19%). Of 80 papers that reported on specific collaborations, most were descriptive reports (51.3%). Research studies represented 34 papers: 31% were program evaluations, 9% were literature reviews and 9% were discussion papers. Key strategies to ensure rigor in conducting a scoping literature review while engaging a large geographically dispersed team are presented for each stage. The use of enabling technologies was essential to managing the process. Leadership in championing the use of technologies and a clear governance structure were necessary for their successful uptake.
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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 | 0.232 | 0.303 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.086 | 0.057 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".