Providing specialized midwifery telemedicine services during the COVID-19 pandemic
Bibliographic record
Abstract
search, G -Funds CollectionBackground.The COVID-19 crisis encouraged policymakers, regulators and payers to use remote health care.Objectives.This study was conducted to investigate the awareness and practice of midwifery as a remote service during the COVID-19 pandemic.Material and methods.This is a descriptive research in which the views and practices of 600 midwives from all over Iran were assessed using a web-based questionnaire as a data collection tool.A hyperlink to the questionnaire was shared in social media groups dedicated to midwife members.Results.62.7% of the midwives participating in the study considered telemedicine to be applicable in health services.However, most of them still provided specialised services in person during the COVID-19 pandemic, and provision of remote services was limited to telephone counselling.The awareness and practice of midwives in this field were not appropriate.A significant relationship of midwives' awareness and practice with their age, previous work and education was observed (p < 0.01).Conclusions.As far as provision of specialised midwifery services through telemedicine is concerned, the awareness and practice of midwives are limited to the provision of telephone counselling, thus it is necessary to provide technological and educational infrastructure to set the stage for providing comprehensive midwifery services through telemedicine.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".