Assessing the potential use of blockchain technology to improve the sharing of public health data in a western Canadian province
Bibliographic record
Abstract
This exploratory, qualitative study set out to identify the encountered and perceived barriers to public health (PH) data sharing in a Canadian province with a view to assessing blockchain technology as a potential solution. A topic guide was developed, based on previous research in the area. This was then utilised for ten in-depth, semi-structured interviews with PH professionals between 27 May and 18 June 2019. Each stage of research was congruent with the philosophical underpinning of Gadamerian hermeneutic phenomenology. The major themes that emerged from the data collected were related to the information systems in use, data quality and ownership, as well as client identity management. The recurring core theme throughout all interviews was related to ineffective leadership and management, contributing to each major theme. Overwhelmingly the results show that the majority of barriers faced in this province are human-related. It is concluded that while blockchain technology shows promise for enhancing data sharing in healthcare, it is still many years away from being implemented in this Canadian province. As the results of this study indicate, there are human related barriers that could be addressed in the meantime, which are outside the scope of a technical solution. Future work should explore the perspectives of other stakeholders, such as the provincial government to fully understand the potential for using blockchain to share PH data in this province.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".