Family attitudes towards an electronic personal health record in a long term care facility
Bibliographic record
Abstract
Objective: To explore the perceptions of family members regarding the importance of an electronic personal health record (EPHR) called MyChart as a healthcare information source to support the care of their loved ones within a long term care (LTC) facility in Toronto, Ontario.Methods: One hundred and fifty eight family members of the patients in six LTC units at the hospital were given a survey to determine their perceptions regarding the utility of the content items within an EPHR that was recently adopted by the LTC institution.Results: The response rate was 41% (n = 65). Many family members (n = 48) felt it was important to have access to their loved one’s EPHR. Respondents ranked test results (38%; n = 25), doctor’s clinical notes (26%; n = 17), medication lists (15%; n = 10) and upcoming appointments (11%; n = 7) as the number one most important content item that they would want to have access to. In addition to the standard content items found within an EPHR, family members requested electronic access to a variety of additional medical content items that are not currently offered within the EPHR, such as status alerts. Overall, they felt that an EPHR would enhance communication between the care team and the family, however 30% of family members identified concerns linked to security and confidentiality of the electronic health record information.Conclusions: Family members felt that an EPHR would be an important tool in the LTC facility to assist with information exchange between care providers and the family. It is important to consider that the additional information requested beyond what is traditionally found in an EPHR as well as the specific communication concerns raised, may be limited to a LTC setting.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".