‘Snapshot in time’: a cross-sectional study exploring stakeholder experiences with environmental scans in health services delivery research
Bibliographic record
Abstract
Objective To describe stakeholder characteristics and perspectives about experiences, challenges and information needs related to the use of environmental scans (ESs). Design Cross-sectional study. Setting and participants A web-based survey platform was used to disseminate an online survey to stakeholders who had experience with conducting ESs in a health services delivery context (eg, researchers, policy makers, practitioners). Participants were recruited through purposive and snowball sampling. The survey was disseminated internationally, was available in English and French, and remained open for 6 weeks (15 October to 30 November 2022). Analysis Descriptive statistics were used to describe the characteristics and experiences of stakeholders. Thematic analysis was used to analyse the open-text questions. Results Of 47 participants who responded to the survey, 94% were from Canada, 4% from the USA and 2% from Australia. Respondents represented academic institutions (57%), health agency/government (32%) and non-government organisations or agencies (11%). Three themes were identified: (a) having a sense of value and utility ; (b) experiencing uncertainty and confusion ; and (c) seeking guidance . The data suggest stakeholders found value and utility in ESs and conducted them for varied purposes including to: (a) enhance knowledge, understanding and learning about the current landscape or state of various features of health services delivery (eg, programmes, practices, policies, services, best practices); (b) expose needs, service barriers, challenges, gaps, threats, opportunities; (c) help guide action for planning, policy and programme development; and (d) inform recommendations and decision-making. Stakeholders also experienced conceptual, methodological and practical barriers when conducting ESs, and expressed a need for methodological guidance delivered through published guidelines, checklists and other means. Conclusion ESs have value and utility for addressing health services delivery concerns, but conceptual and methodological challenges exist. Further research is needed to help advance the ES as a distinct design that provides a systematic approach to planning and conducting ESs.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".