Connecting for Care: a protocol for a mixed-method social network analysis to advance knowledge translation in the field of child development and rehabilitation
Bibliographic record
Abstract
BACKGROUND: Connections between individuals and organizations can impact knowledge translation (KT). This finding has led to growing interest in the study of social networks as drivers of KT. Social networks are formed by the patterns of relationships or connections generated through interactions. These connections can be studied using social network analysis (SNA) methodologies. The relatively small yet diverse community in the field of child development and rehabilitation (CD&R) in Canada offers an ideal case study for applying SNA. The purposes of this work are to (1) quantify and map the structure of Canadian CD&R KT networks among four groups: families, health care providers, KT support personnel, and researchers; (2) explore participant perspectives of the network structure and of KT barriers and facilitators within it; and (3) generate recommendations to improve KT capacity within and between groups. Aligning with the principles of integrated KT, we have assembled a national team whose members contribute throughout the research and KT process, with representation from the four participant groups. METHODS: A sequential, explanatory mixed-method study, within the bounds of a national case study in the field of CD&R. Objective 1: A national SNA survey of family members with advocacy/partnership experience, health care providers, KT support personnel, and researchers, paired with an anonymous survey for family member without partnership experience, will gather data to describe the KT networks within and between groups and identify barriers and facilitators of network connections. Objective 2: Purposive sampling from Phase 1 will identify semi-structured interview participants with whom to examine conventional and network-driven KT barriers, facilitators, and mitigating strategies. Objective 3: Intervention mapping and a Delphi process will generate recommendations for network and conventional interventions to strengthen the network and facilitate KT. DISCUSSION: This study will integrate network and KT theory in mapping the structure of the CD&R KT network, enhance our understanding of conventional and network-focused KT barriers and facilitators, and provide recommendations to strengthen KT networks. Recommendations can be applied and tested within the field of CD&R to improve KT, with the aim of ensuring children achieve the best health outcomes possible through timely access to effective healthcare.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Other design | high |
| grok | Scholarly communication Domain: not available · Genre: Protocol About the Canadian research system: yes · About a Canadian topic: yes | Other design | high |
| opus | Scholarly communication Domain: not available · Genre: Protocol About the Canadian research system: yes · About a Canadian topic: yes | Other design | medium |
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 | 0.127 | 0.115 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.103 | 0.025 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".