Use of environmental scans in health services delivery research: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To examine the extent and nature of evidence on the use of the environmental scan (ES) in the health services delivery literature. DESIGN: Scoping review. METHODS: . A Peer Review of Electronic Search Strategies was completed. Seven electronic databases and the grey literature were searched. Pairs of researchers independently performed two levels of screening and data extraction. Data were analysed using qualitative content and thematic analysis. RESULTS: Ninety-six studies were included in the scoping review. Researchers conducted ESs for many purposes, the most common being to examine the current state of programmes, services or policies. Recommendations were informed by ESs in 20% of studies. Most common data collection methods were literature review (71%), key informant or semistructured interviews (46%) and surveys (35%). Over half (53%) of the studies used a combination of passive (looking at information eg, literature, policies, guidelines) and active (looking for information eg, surveys, interviews) approaches to data collection. Person sources of data (eg, healthcare stakeholders, community representatives) and non-person sources of data (eg, documents, electronic databases, the web) were drawn on to a similar extent. The thematic analysis of the definitions/descriptions yielded several themes including instrument of discovery, knowledge synthesis, forward-looking and decision making. Research gaps identified included absence of a standard definition, inconsistencies in terminology and lack of guiding frameworks in the health services delivery context. CONCLUSION: ESs were conducted to gather evidence and to help inform decision making on a range of policy and health services delivery issues across the continuum of care. Consistency in terminology, a consensus definition and more guidance on ES design may help provide structure for researchers and other stakeholders, and ultimately advance ES as a methodological approach. A working definition of ES in a health services delivery context is presented.
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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 | 0.175 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.071 | 0.058 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 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".