Collaborative leadership and the implementation of community-based fall prevention initiatives: a multiple case study of public health practice within community groups
Bibliographic record
Abstract
BACKGROUND: Falls among community-dwelling older adults are a serious public health concern. While evidence-based fall prevention strategies are available, their effective implementation requires broad cross-sector coordination that is beyond the capacity of any single institution or organization. Community groups comprised of diverse stakeholders that include public health, care providers from the public and private sectors and citizen volunteers are working to deliver locally-based fall prevention. These groups are examples of collective impact and are important venues for public health professionals (PHPs) to deliver their mandate to work collaboratively towards achieving improved health outcomes. This study explores the process of community-based group work directed towards fall prevention, and it focuses particular attention on the collaborative leadership practices of PHPs, in order to advance understanding of the competencies required for collective impact. METHODS: Four community groups, located in Ontario, Canada, were studied using an exploratory, retrospective, multiple case study design. The criteria for inclusion were presence of a PHP, a diverse membership and the completion of an initiative that fit within the scope of the World Health Organization Fall Prevention Model. Data were collected using interviews (n = 26), focus groups (n = 4), and documents. Cross-case synthesis was conducted by a collaborative team of researchers. RESULTS: The community groups differed by membership, the role of the PHP and the type of fall prevention initiatives. Seven practice themes emerged: (1) tailoring to address context; (2) making connections; (3) enabling communication; (4) shaping a vision; (5) skill-building to mobilize and take action; (6) orchestrating people and projects; and (7) contributing information and experience. The value of recognized leadership competencies was underscored and the vital role of institutional supports was highlighted. CONCLUSION: To align stakeholders working towards fall prevention for community-dwelling older adults and establish a foundation for collective impact, public health professionals employed practices that reflected a collaborative leadership style. Looking ahead, public health professionals will want to shift their focus to evaluating the effectiveness of their group work within communities. They will also need to assess outcomes and evaluate whether the anticipated reductions in fall rates among community-dwelling older adults is being achieved.
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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 | 0.067 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".