Support for Collective Wellbeing: A Path to Providing Integrated Care in Rural Communities
Bibliographic record
Abstract
Audience: The Connecting People and Community for Living Well (CPCLW) model aims to transform how health, social and community sectors in rural communities work locally to design, deliver, and implement services and supports so those in their community affected by dementia can ‘live well’. There is growing awareness in Alberta, Canada of the need for sectors to work together to support those in their homes and communities. This is especially important in rural communities where fewer resources exist. Multi-sector community teams were seen as a necessary part of this structure, so there was interest in understanding what supported their collective wellbeing, and how supporting it could sustain their ability to impact the wellbeing of those living in the community. The model offers a structure for teams to build and sustain their wellbeing. Engagement and actions: This initiative partnered with five rural communities. Community team participants included individuals from the health care sector, non-profit organizations, community organizations, those with lived experience, and municipal organizations. Focus groups learnings provided insight into what contributed to collective wellbeing. Learnings, and a literature search informed development of a Wellbeing Guide, and iterations were tested by participating teams. A final version is being used by teams to identify priorities and develop a collaborative action plan. In conjunction with developing the Guide, a competency framework was developed and includes 10 competencies capturing skills, knowledge and behaviors required to work successfully in multi-sector teams. Results: Together, the components of the CPCLW model support individual and collective wellbeing and include provincial support; communities advancing local work; 10 core principles; Wellbeing Guide; Competency Framework. While information gathering is ongoing, results so far include identification of 10 contributors to collective wellbeing, and learnings from the teams’ experience using the Guide, and implementing the model. Key findings include a formal representation of prioritization, planning and implementation of activities; identification of key relationships; and illustration of local challenges that may need system and policy changes. Next steps: Broader work of this initiative includes understanding and supporting wellbeing of those in the community affected by dementia. Next steps include expanding the Guide to include other underserved populations. Work is underway to complete both a forecast and evaluative social return on investment of the model which will help illustrate its value in supporting wellbeing, and other social outcomes. Learnings: The CPCLW model shows promise in transforming how underserved groups in rural communities are supported to live well in their homes and communities for as long as possible. Learnings include: increased understanding of collective wellbeing of multi-sector teams; relationships are core to success; dedicated resources help achieve short and long term successes; collaboration in prioritizing, planning, and implementing actions is needed; support from a system level team contributes to the teams’ success and sustainability; the Wellbeing Guide can be useful to demonstrate impact of actions taken; and early indications of necessary system and policy changes.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".