Mobile Phone Access and Preferences Among Medical Inpatients at an Urban Canadian Hospital: A Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Background: Digital health interventions are increasingly used for patient care, yet little data is available on the phone access type and usage preferences of medical ward patients to inform the most appropriate digital interventions.Methods: To learn about mobile phone ownership, internet access, and cellular use preferences among medical patients, we conducted a researcher-administered survey of patients admitted to five internal medicine units at Vancouver General Hospital (VGH) in January 2020.Results: A total of 81 ward patients completed the questionnaire from the two survey dates. Of those, 63.0% owned their own mobile phone, an additional 22.2% had access to a mobile phone via a proxy (or an authorized third-party) such as a family member, and 14.8% did not own or have access to a mobile phone. All participants with mobile phone access had cellular plans (i.e., phone and text) ; however, a quarter of respondents did not have data plans with internet. 71.1% of men owned a mobile phone compared to only 52.8% of women. All participants at a ‘high’ risk of readmission had access to a mobile phone, either as phone-owners or proxy-dependent users.Conclusion: Access to mobile phones among medical ward patients was high, but incomplete. More patients had cellular than data plans (i.e., internet and applications). Understanding patient-specific access is key to informing potential uptake of digital health interventions aimed at using patients’ mobile phones (mHealth) from an effectiveness and equity lens.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".