Fertility Workup With Video Consultation During the COVID-19 Pandemic: Pilot Quantitative and Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Due to the COVID-19 pandemic, major parts of elective health care in the Netherlands, such as reproductive medicine, were paused. When health care was resumed, video consultation was used as a new solution to continue consultations with the new governmental rules of social distancing. Prior to this COVID-19 situation, video consultation was not used extensively in the Netherlands; therefore, physicians and patients are not familiar with this way of consultation. OBJECTIVE: The purpose of this study was to measure the level of patient centeredness and shared decision making in infertile couples who have undergone fertility workup through video consultation. METHODS: This is a questionnaire study with an additional qualitative part for a more in depth understanding. Infertile couples (ie, male and female partners with an unfulfilled wish for a child after 1 year of unprotected intercourse) were referred to a fertility center and underwent fertility workup through video consultation. The fertility workup consisted of 2 separate video consultations, with diagnostic tests according to a protocol. After the last video consultation couples received a digital questionnaire, which consisted of a modified version of the Patient-Centered Questionnaire-Infertility (PCQ-I) and CollaboRATE questionnaire. Fifty-three eligible infertile couples were approached, and of these, 22 participated. Four women were approached for a semistructured interview. RESULTS: The median score on the modified PCQ-I (scale of 0 to 3) was 2.64. The highest rating was for the subscale communication and information, and the lowest rating was for the subscale organization of care. The median score on the CollaboRATE questionnaire (scale of 1 to 9) was 8 for all 3 subquestions. Patients mentioned privacy, less travel time, and easy use of the program as possible benefits of video consultation. However, patients preferred the first consultation with their physician to be face-to-face consultation as video consultation was considered less personal. CONCLUSIONS: The high levels of patient centeredness and shared decision making show that video consultation is a promising way of providing care remotely, although attention has to be payed to mitigate the more impersonal setting of video consultation when compared with face-to-face consultation.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".