MétaCan
Menu
Back to cohort
Record W4402588925 · doi:10.37464/2024.413.816

An environmental scan of studies reporting current practices for the conduct of environmental scans

2024· article· en· W4402588925 on OpenAlexfundno aff
Tram Nguyen, Briano Di Rezze, Heather Colquhoun

Bibliographic record

VenueAustralian journal of advanced nursing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchFulbright CanadaMcMaster University
KeywordsEnvironmental reportingCurrent (fluid)Project commissioningPublishingMedicineBusinessMedical physicsEnvironmental planningAccountingPolitical scienceEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

Objective: The objective of this environmental scan is to synthesize the published, peer-reviewed literature specific to the term ‘environmental scan’ to determine how it is currently being used in health research and to propose some promising practices. Background: Environmental scans are becoming increasingly popular in synthesizing information on emergent topics and describing practice and research scope. Despite the growing use of environmental scans in health research, including nursing and rehabilitation, limited attention is given to methodological best practices. It is essential that we develop knowledge in this area to assist researchers, trainees, healthcare professionals, educators, and decision-makers with the use and reporting of environmental scans. Study design and methods: This environmental scan included a search of four health databases: CINAHL, Embase, MEDLINE, and PsycINFO. We included peer-reviewed studies published between 2000-2024 in English using two key terms, ‘environmental scan’ and ‘health’. Studies were included that described methods used in conducting an environmental scan. Results: We identified 56 studies describing methods for conducting environmental scans. A synthesis of these studies revealed four promising practices: 1) consider environmental/contextual influences, 2) use of multiple data sources and approaches, 3) engage stakeholders to ensure relevance/need and increase uptake, and 4) use of outcomes to address knowledge or service gap to optimise impact. Conclusion: The findings of this environmental scan are among the first to examine methodological studies to determine promising practices for conducting environmental scans across health disciplines. Implications for research, policy, and practice: The findings of this novel environmental scan are beneficial for health professionals, researchers, trainees, educators, and decision-makers in informing research, practice, and policy change What is already known about the topic? The use of environmental scans is becoming increasingly popular in health research, including nursing and rehabilitation. There is a lack of consistency in the use and reporting of environmental scans across health disciplines. What this paper adds This environmental scan is among the first to contribute foundational knowledge and innovation in promising best practices for the conduct of environmental scans in health research. The novel findings will assist in promoting consistency in the use and reporting of environmental scans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.396
GPT teacher head0.594
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAustralian journal of advanced nursingSame topicDelphi Technique in ResearchFrench-language works237,207