A Mixed-Methods <i>Quick Strike</i> Research Protocol to Learn About Children With Complex Health Conditions and Their Families
Bibliographic record
Abstract
Advances have been made to improve health care for children with complex health conditions (CCHCs); however, little is known of the needs of these children and their families in the Canadian context. In this article, we describe our Canadian Institutes of Health Research funded Quick Strike protocol, a mixed-methods multisite research project that explored CCHC and their families in two Canadian provinces. The aims were (a) to describe and define CCHC, (b) to understand the needs of CCHC and their families, (c) to identify gaps and barriers to services for this population, and (d) to adapt and test the application of a computerized algorithm to yield information on CCHC. The mixed-methods design was comprised of four components: three qualitative and one quantitative. We describe the components of this project and outline the methods and procedures of data collection and analysis for each component. One of the main sources of data was interviews from 121 stakeholders, which included CCHC and family members, as well as health, social, and education professionals. This Quick Strike project was designed to engage stakeholders and the public with integrated knowledge translation threaded as a core element throughout the research process. Multiple strategies were used to validate and disseminate early findings from the research. As we outline in this article, this research project provided the foundation for one innovative service model of care, NaviCare/SoinsNavi, and spawned a number of additional outcomes such as a secondary analysis of the data to describe interprofessional collaboration for CCHC.
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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 | 0.036 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".