Professional ambivalence among care workers: The case of doula practice
Bibliographic record
Abstract
Doulas are non-medical, privately paid caregivers to women during pregnancy and childbirth, who have entered the maternity care field in recent decades. In a hospital setting, doulas offer women emotional and physical support that supplements clinical care. Drawing on focus groups and interviews with eight doulas working in one Atlantic Canadian city, along with Abbott and Merrabeau's analysis of the professionalization of 'caring' occupations, I consider how doulas navigate the uncertain terrain of their emerging occupation. In general, the work performed by care workers is viewed as an extension of the work women perform in the domestic sphere, for which they are understood to be 'naturally' talented. As a result, 'caring' occupations need to find ways to emphasize the value of their role and justify the need for adequate pay. While traditional processes of professionalization appear to offer a solution, the credentials associated with being a 'profession' - a monopoly over a field and a distinct body of (scientific) knowledge - may not be relevant when evaluating the quality of care provision. I argue that doulas hold ambivalent perspectives towards the nature of their training, the requirements of certification and 'appropriate' interactions with clients due to the broader tension between care work and professional ideology.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.059 | 0.061 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.012 |
| 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".