Bibliographic record
Abstract
Childbirth is an emotionally complex experience. Some women seek additional support during pregnancy and labour, beyond what is typically offered in a hospital setting. Birth doulas fill this role by providing continuous emotional and physical support during labour. Based on interviews with 26 doulas practicing in Toronto, Canada, this paper examines how the work of providing emotional support to women during childbirth and avoiding conflict with hospital staff requires significant emotional labour - the purposeful management of emotion to incite certain feelings in clients or customers. Doulas perform emotional labour to accomplish two main tasks: managing their client's emotions during childbirth to help create positive birth experiences, and concealing emotions from hospital staff to avoid generating conflict. The inherently emotional context of childbirth also complicates demands for emotional labour, since doulas must balance their genuine emotional reactions to witnessing someone give birth with carefully managing their own affect to encourage particular feelings in their clients. Taken together, these findings indicate that doulas navigate a complex web of "feeling rules" that requires them to oscillate between manufactured emotion and authentic feeling - a demand that can be mentally exhausting. Overall, the emotional labour they perform seems to require a great deal of effort and skill - work that is often invisible and devalued. This study reinforces the importance of social support during the perinatal period by demonstrating that doulas attempt to "create" positive birth experiences, despite not playing a role in the medical management of the labour.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".