Seniors' views on the use of electronic health records
Bibliographic record
Abstract
In the Mauricie and Centre-du-Québec region of the province of Quebec, Canada, an integrated services network has been implemented for frail seniors. It combines three of the best practices in the field of integrated services, namely: single-entry point, case management and personalized care plan. A shared interdisciplinary electronic health record (EHR) system was set up in 1998. A consensus on the relevance of using EHRs is growing in Quebec, in Canada and around the world. However, technology has out-paced interest in the notions of confidentiality, informed consent and the impact perceived by the clientele. This study specifically examines how frail seniors perceive these issues related to an EHR. The conceptual framework is inspired by the DeLone and McLean model whose main attributes are: system quality, information quality, utilisation modes and the impact on organisations and individuals. This last attribute is the focus of this study, which is a descriptive with quantitative and qualitative component. Thirty seniors were surveyed. Positive information they provided falls under three headings: (i) being better informed; (ii) trust and consideration for professionals; and (iii) appreciation of innovation. The opinions of the seniors are generally favourable regarding the use of computers and the EHR in their presence. Improvements in EHR systems for seniors can be encouraged.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".