Bibliographic record
Abstract
The aim of this thesis was to develop a valid, reliable, and nationwide usable obstetric telephone triage system for unplanned care requests of pregnant women. Also, we wanted to evaluate the use of the system by healthcare professionals within different hospital settings and patients’ experiences of the system. This thesis contributes to the improvement of current obstetric care processes. This is necessary in view of the increased volume per obstetric emergency care department, the pursuit of high-quality interpretation and documentation of unplanned obstetric care consultations. A telephone triage system adds to this need. As stated in the main findings it can be said that The Dutch Obstetric Telephone Triage System (DOTTS) achieved consensus of healthcare professionals after its development with relevant stakeholders (chapter 2). DOTTS consists of five presenting symptoms: fluid loss, vaginal bleeding, abdominal pain, concerned pregnant/non somatic symptoms and other physical symptoms. DOTTS consists also of five urgency levels: resuscitation & life threatening, emergency, urgent, non-urgent and self-care advice. This thesis showed that a valid (chapter 3) and reliable (chapter 4) estimate of the urgency levels can be made by using DOTTS and that the use of DOTTS can be safely encouraged in obstetric practice. Further, that the use of a valid and reliable telephone triage system contributes to the correct distribution of human and financial resources. In chapter 5 addition, insight into professionals experiences provided valuable information regarding the aspects that support implementation of DOTTS (chapter 5) in practice. Lastly, in an examination of patients’ experience we found that patients perceived that professionals use DOTTS without losing the human touch (chapter 6). In the general discussion three subjects will be discussed: 1) DOTTS as an example of a successful health care transformation, 2) Aspects of successful innovations that contribute to transformation and, 3) Theoretical concepts of multidisciplinary stakeholder engagement and multidisciplinary learning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".