A cross-sectional survey exploring attitudes towards provincial electronic health record implementation among clients attending the Provincial Sexually Transmitted Infections Clinic in British Columbia
Bibliographic record
Abstract
INTRODUCTION: To support control of sexually transmitted infections (STI), British Columbia is exploring data sharing through a provincial electronic health record (EHR). Increased electronic data sharing among healthcare providers may be a barrier for clients of STI clinics where expectations of privacy and confidentiality are heightened. METHODS: A survey to assess attitudes towards sharing of personal health information through a provincial EHR was conducted with a convenience sample of clients attending an STI clinic in Vancouver. Descriptive statistics and logistic regression were used to examine the association between the primary outcome variable, whether a client would be less likely to take the test for an STI or HIV if their clinic records were made part of a provincial EHR, and independent variables of interest. RESULTS: A total of 1004 clients completed the survey, and 31% stated that they would be less likely to get tested for STI/HIV if their records were made part of a provincial EHR. Participants who were more likely to agree that they might avoid testing had the expectation that no other clinician would review their chart (adjusted OR (AOR)=3.55), or expected their records would not be shared beyond the clinic (AOR=2.81). DISCUSSION: While most participants thought an EHR was acceptable, a large minority expressed that they might avoid testing if their records were made part of a provincial EHR. The introduction of a provincial EHR should be considered with caution in STI healthcare settings to ensure that screening, case detection and treatment are not negatively impacted.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".