Managing information and knowledge within maternity services: Privacy and consent issues
Bibliographic record
Abstract
OBJECTIVE: Electronic Patient Records have improved vastly the quality and efficiency of care delivered. However, the formation of single demographic database and the ease of electronic information sharing give rise to many concerns including issues of consent, by whom and how data are accessed and used. This paper examines the organizational and socio-technical issues related to privacy, confidentiality and security when employing electronic records within a maternity service hospital in England. METHODS: A preliminary questionnaire was administered (n = 52), in total, 24 responses were received. Sixteen responses were from personnel in the information technology department, 5 from health information department and 3 from midwifery managers. This was followed by a semi-structured interview with representatives from the clinical and technological side. RESULTS: A number of issues related to information governance (IG) have been identified, especially breaches on sharing personal information without consent from the patients have been identified as one immediate challenge that needs to be fixed. CONCLUSION: There is an immediate need for more robust, realistic, built-in accountability both locally and nationally on data sharing. A culture of ownership and strict adherence to IG principles is paramount. Focused training in the area of data, information and knowledge sharing will bring in a balance of legitimate usage against the individual's rights to confidentiality and privacy.
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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 | 0.076 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".