20 Follow-Up after a Paediatric Emergency Department Visit – Telephone versus Electronic Mail
Bibliographic record
Abstract
The Internet has become an unlimited source of health related information and revolutionised health information access by parents. Follow-up after an Emergency Department (ED) visit is important for continuity of care, monitoring changes in patient health and for informing patients of test results taken during their visit. We conducted this study to determine if electronic main (e-mail) would be better than phone for follow-up. During two months, research assistants recruited parents of children being discharged from a large tertiary ED in Toronto, Ontario, that have a personal e-mail account and a personal telephone line. Parents were randomized to e-mail or phone follow-up, that included two questions. Parents not replying to the e-mail were contacted via telephone after 10 days. The main outcome measure was the response rate. Data was collected on Microsoft Excel and statistical analyses used SPSS for calculation of response rate and comparisons between the phone and e-mail groups. A total of 337 families were approached. Two hundred and sixty five (79%) had Internet access, personal e-mail and telephone line available and 75% of them check e-mails at least once a day. A total of 198 families were randomized for a follow-up. Of 98 families, 85 (87%) were reached by phone within average of 17 hours (range 0–98). Of 100 families contacted via e-mail only 53 (53%) responded in a mean of 46 hours (range 0–242) (p<0.0005). Ten (10%) of the e-mails “bounced back”. Of the parents in the e-mail group that had received the e-mail and did not respond 21 (57%) did not check the e-mail, did not remember reading the e-mail or had problems with e-mail access, 1 (3%) did not have time to respond and 15 (40%) were not reached even by phone. E-mail failed to be better than phone in reaching families for follow-up after a Paediatric ED visit. It could be used for non-urgent messages by about half of our patient population that can access the Internet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".