Why is information governance important for electronic healthcare systems? A Canadian experience
Bibliographic record
Abstract
The objective of this paper is to propose an information governance model for the Ontario health care system.This study irst deines information governance, describes information governance maturity level, and introduces the data governance model to achieve the goal.Then it explains the information governance model for the Ontario healthcare system, applies the model to a case study, and demonstrates how the model can be applied to identify key problems and suggest a future action plan.Using the Canadian healthcare system as the backdrop, the study, drawing on the eight principles of information governance outlined by the Association of Records Managers and Administrators (ARMA) and the Data Governance Model, proposes an information governance framework detailing how information should be governed from four dimensions: people, process, policy, and technology.The model is then applied to analyze a case study on the 18-month well-baby visit program.After analyzing the indings from the case, the paper concludes with the implications for healthcare practitioners.The study contributes to the academic study on information governance by offering a well-deined model to practitioners by suggesting effective approaches to information governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".