Challenges and Facilitating Factors in Accessing Administrative Data for Research: Insights from the Children's Health Profile and Trajectory Initiative in NB and PEI
Bibliographic record
Abstract
IntroductionAdministrative health data (AHD) are typically not analyzed to produce evidence on the effectiveness and limitations of primary prevention programs and strategies. The value of AHD for answering research questions is generally recognized, but the challenges in accessing and using these data for research are not always know and documented. Objectives and ApproachTo identify and advise on the facilitating factors and challenges of accessing select AHD in New Brunswick (NB) and Prince Edward Island (PEI) for the purpose of creating an intra-provincial Child Health Profile (CHP) and population-based birth cohort database, using existing AHD not been previously linked. This research is a cross-jurisdictional collaboration between NB and PEI with an integrated knowledge translation (iKT) approach that adheres to each province’s unique data policies, data procedures, and data governance. The collaboration involves people in various roles: provincial government managers, policy-makers, data custodians, health practitioners, citizens, community organizations, in addition to academic researchers. ResultsAccess to select AHD required considerable preparation, cross province coordination, and ongoing discussions over many months. Key facilitators were the NB Institute for Research, Data and Training, a newly established data repository that holds provincial AHD in NB, and the provincial health authority in PEI. In NB, the existence of well-documented protocols and support from designated personnel (including trained data analysts) were assets facilitating data access through the data repository. In PEI, REB approval was obtained more rapidly but challenges occurred in subsequent stages of data access directly through the health authority. This research supports the empowerment of stakeholders such as Public Health and researchers who are trying to leverage ‘big data’ resources to address research and practice questions regarding children’s health. Conclusion/ImplicationsAccessing AHD for the project was facilitated by the existence of well-documented protocols and other specialized resources that help streamline the process of data sharing while ensuring data privacy and security. Continued relationship-building among stakeholders is needed to facilitate and maximize the use of existing AHD in NB and PEI.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".