Development of a Chronic Care Model for Neurological Conditions (CCM-NC)
Bibliographic record
Abstract
BACKGROUND: Persons with neurological conditions and their families face a number of challenges with the provision of health and community-based services. The purpose of this study was to understand the existing health and community service needs and gaps in care and to use this information to develop a model to specify factors and processes that may improve the quality of care and health and well-being for persons with neurological conditions. METHODS: We conducted semi-structured interviews with health care professionals, community-based non-health care professionals working with individuals with neurological conditions, and policy makers -from the Ministries of Health, Community and Social Services, Transportation and Education- across Canada. We used a purposive sampling and snowballing approach to obtain maximum variation across professions, sector and geography (provinces and territories, rural and urban). Data analysis was an iterative, constant comparative process involving descriptive and interpretive analyses and was initially guided by the components of the Expanded Chronic Care Model. RESULTS: A total of 180 individuals completed the interviews: 39% (n = 70) health care professionals, 47% (n = 85) community-based non-health care professionals, and 14% (n = 25) policy makers. Based on the data we developed the Chronic Care Model for Neurological Conditions (CCM-NC). The major needs/gaps are represented by the following themes: acceptance and openness to neurological conditions, evidence informed policy, investments and funding, supported transitions, caregiver support, and life enhancing resources (education, employment, housing and transportation), knowledge and awareness of neurological conditions and availability and access to health services. The model maintains that intersectoral collaboration across the health system, community and policy components is needed. It recognizes that attitudes, policies, enhanced community integration and health system changes are needed to develop activated patients and families, proactive service delivery teams, a person-centred health system and healthy public policy for persons with neurological conditions. CONCLUSION: The CCM-NC will generate debate and discussion about the actions needed in each of the model components to enable people with neurological conditions to sustain healthier lives. Next steps include validating the model with persons with neurological conditions, in and outside of the Canadian context and developing and evaluating interventions to test the model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".