Using Network Analysis to Understand and Advance Falls Prevention Services and Programs
Bibliographic record
Abstract
The purpose of this study was to understand referral linkages that exist among falls prevention agencies in a southern Ontario region using network analysis theory. This was a single case study which included fifteen individual interviews. The data was analyzed through the constant comparative approach. Ten themes emerged and are classified into internal and external factors. Themes associated with internal factors are: 1) health professionals initiating services; 2) communication strategies; 3) formal partnerships; 4) trust; 5) program awareness; and 6) referral policies. Themes associated with external factors are: 1) client characteristics; 2) primary and community care collaboration; 3) networking; and 4) funding. Recommendations to improve the referral pathway are: 1) electronic database; 2) electronic referral forms; 3) educating office staff; and 4) education days. This study outlined the benefit of using network analysis to understand referral pathways and the importance of implementing strategies that will improve falls prevention referral pathways.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".