Diagnostic classification of fear of childbirth: why specific phobia may not be enough
Bibliographic record
Abstract
Abstract Background: Fear of childbirth (FoB) is a common experience during pregnancy which can cause clinically significant distress and impairment. To date, a number of investigations of FoB have assumed that clinically significant FoB is best understood as a type of specific phobia. However, preliminary evidence suggests that specific phobia may not be the only diagnostic category under which clinically significant symptoms of FoB are best described. Aim: The current study is the first to investigate which DSM-5 diagnostic categories best describe clinically significant symptoms of FoB. Method: Pregnant people reporting high levels of FoB ( n =18) were administered diagnostic interviews related to their experience of FoB. Results: Participants ( n =18) were predominantly nulliparous (73.3%), cisgender women (83.3%). Of these, 14 (77.8%) met criteria for one or more DSM-5 anxiety-related disorders. Preliminary findings suggest that primary FoB may align with specific phobia criteria, whereas secondary FoB (following a traumatic birth) may be better classified under post-traumatic stress disorder (PTSD). FoB also featured in other anxiety-related disorders but was not the primary focus (e.g. obsessive-compulsive disorder). Four participants did not meet criteria for any DSM- 5 disorder. Conclusions: Findings provide preliminary evidence that clinically significant FoB fits within existing DSM-5 categories, in particular specific phobia and PTSD. Although FoB-related concerns appears in other anxiety-related disorder categories, it does not appear as the primary focus. Although informative, due to the small sample employed in this research, replication in larger and more diverse samples is needed.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".