Barriers and enablers of dementia training in healthcare workers in rural and remote Australia: A scoping review to inform future approaches to training
Bibliographic record
Abstract
INTRODUCTION: Dementia is now responsible for the greatest burden of disease of any chronic illness in older Australians. Rural and remote communities bear the impacts of this disproportionately. Additional training and education for healthcare staff to support people living with dementia is needed. OBJECTIVE: The objective of this scoping review was to map and synthesise the evidence related to barriers and enablers of accessing dementia training for Australian healthcare workers located in rural and remote areas. DESIGN: This scoping review systematically searched multiple databases in January 2023 for peer-reviewed literature on the topic. Reviewers used Covidence to screen titles and abstracts of located sources, and to screen full-text articles. FINDINGS: From 187 articles screened, seven peer-reviewed journal articles were included in the final data analysis; all were from Australia or Canada. The most common barrier described was low staffing, precluding release of staff for dementia training. Enablers to participation in dementia training were availability of online training programs, as well as training providers collaborating with end users to ensure the training met their learning needs. DISCUSSION: This review provides evidence of barriers and enablers specific to rural and remote healthcare workers accessing dementia training. It also explores other approaches to training that have been trialled successfully in different settings. CONCLUSION: Addressing the identified barriers and enablers may assist in developing training approaches appropriate for existing staff, and in meeting training needs for the future workforce.
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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 | 0.041 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".