Plugged-in: a Canadian survey of technology ownership, access, use, and attitudes among emergency department patients
Bibliographic record
Abstract
Introduction: Patient-facing digital health technologies have the capacity to remedy some of the challenges faced by overburdened and under-resourced Canadian emergency departments (ED). However, the successful implementation of such innovations is dependent on patients' willingness and ability to access and use digital technologies. Moreover, the potential presence of digital disparities in local communities may create or exacerbate inequitable health outcomes. This study aimed to understand technology ownership, access, use, and attitudes among ED patients of a digitally innovative hospital located in an ethnoculturally diverse, urban area of Toronto. Methods: An electronic, self-report, cross-sectional survey was conducted in the ED of an urban, community hospital in Toronto. A convenience sample of ED patients over the age of 18 and proficient in English were invited to participate in the survey between January 3rd and February 14th, 2024. Participants responded to a battery of questions and scales (e.g., the Media and Technology Usage and Attitudes Scale; MTUAS) that were adapted as necessary for this study. Results: The final sample size of 250 participants had a mean age 40.4 ± 16 years, 64.4% were female, and 38% were born outside of Canada. Ownership of at least one digital device was high (97.6%), as was the use of smartphones (96.0%), email (93.6%), text messaging (94.8%), and internet searching (95.6%). Almost all participants had access to the internet (98.0%). Attitudes towards technology were generally positive (mean 4.2/5). There were no significant differences in technology ownership and use based on where participants lived. Few barriers to technology were reported. Conclusion: Despite concerns of digital disparities in an ethnoculturally diverse area of Toronto, technology ownership, access, and use appear to be pervasive among ED patients, irrespective of where they reside. These findings, coupled with patients' generally positive attitudes towards technology, green-light the exploration of patient-facing digital health tools that utilize the digital technology ED patients already own, access, and use to improve the delivery of emergency care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".