Exploring the Experiences of Receiving Pregnancy Care via Telehealth in Ontario: A Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Telehealth is healthcare delivery using telecommunication methods, including audio and virtual calls. In Ontario, Canada during the COVID-19 pandemic, telehealth was utilized in obstetrical care, and many pregnant women had virtual meetings with their healthcare providers (HCP) and monitored their vital signs at home. As the health care system is recovering from the pandemic, it is important to understand the overall experiences of patients who use telehealth services. OBJECTIVE The objective of this qualitative study was to examine the experience of women who used telemedicine for pregnancy care in Ontario. METHODS Semi-structured interviews were conducted with 13 women who used telehealth for their pregnancy or postpartum care within the last four years. Women were asked about benefits and challenges of using telemedicine and offered suggestions for improvement of telehealth services. The interviews were recorded and transcribed verbatim. Thematic analysis was applied to inductively generate key themes from the data. RESULTS Findings suggest that while telemedicine is convenient for seeking medical advice and receiving responses to general questions as well as for removing geographic barriers, it may feel less personal. Some participants also indicated a lack of rapport with their health care providers and felt like they needed to “be their own doctors” when using telehealth. Using mobile applications to schedule meetings, message physicians, and review doctors’ notes after appointments was reported as a benefit of telehealth care, which also improved access to care for participants. These findings suggest that telehealth may offer benefits for some patients and can be used alongside in-person appointments. CONCLUSIONS In conclusion, there are numerous benefits and disadvantages of using telehealth, however there is room for further implementation of telehealth in the obstetrics field.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".