Disaster management in rural and remote primary healthcare settings: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to systematically identify and map the roles of primary healthcare professionals in rural and remote areas during natural, man-made and pandemic disasters. INTRODUCTION: Disasters can be caused by natural events, man-made incidents or infective agents resulting in a pandemic. Healthcare practitioners working in primary care settings have important roles during disaster prevention, preparedness, response and recovery. When rural and remote settings are affected by disasters, there are unique challenges for healthcare professionals. This review will aim to contribute to disaster management knowledge within rural and remote primary health care, and assist in the development of practice-based disaster preparedness and future policy discussion. INCLUSION CRITERIA: This review will consider studies that include primary healthcare professionals, defined as having first-level contact with patients in the community, in rural or remote areas only. The role of the healthcare professional will also be discussed within the paper. Research from Australia, Canada, the USA, New Zealand and the UK will be included. METHODS: Databases to be searched include CINAHL (EBSCOhost), PubMed, Scopus and Embase (Elsevier), as well as gray literature within Trove, MedNar and OpenGrey. The search will be limited to articles written in English and published from 1978 to the present. Titles and abstracts will be screened by two independent reviewers, and full-text studies will be retrieved and assessed against the inclusion criteria. Results will be recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram. Data will be extracted and presented as a tabular summary with supporting narratives and figures.
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 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 | 0.105 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".