Traditions in implementation: case study of falls prevention in a rural health authority
Bibliographic record
Abstract
Introduction Rural Health Authorities are challenged to address the complex issue of falls in aging populations. While extensive evidence exists regarding successful falls prevention interventions, there is little information about planning and implementation at an organisational level. This presentation outlines results from research examining falls prevention at a rural health authority. Method Case study methodology was applied in a phased approach examining development and implementation of the falls prevention strategy. Data collection focused barriers and facilitators experienced by team members, and suggestions to improve processes used. Data collection methods included review of key documents and field notes, participant observation and interviews. The interviews were reviewed, coded and analysed to extract themes. Results The process of strategy development is outlined. The themes for planning reveal the lack of consensus regarding what constitutes a planning process, non-linearity of planning and implementation, and tremendous complexity of barriers and supports. Analysis of implementation identifies themes of the importance of culture within teams and within organisations, a dynamic tension between policy makers and healthcare makers, and competing demands for attention at both individual and organisational levels. Facilitation strategies that contribute to changing everyday practice are discussed, as well as sources of resistance to change. Conclusions This research provides insight for health organisations addressing falls in aging populations, particularly in rural areas which lack well developed resources and traditions for implementation of evidence in routine practices. Translating evidence into everyday practice for injury prevention is complex, and easily overwhelmed by competing demands.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".