Epistemic Injustice in Healthcare: Reevaluating Routine Pregnancy Testing
Bibliographic record
Abstract
The role of testimonial exchange in the patient-physician relationship is fundamental to the health care experience.It is based on principles of trust, and ultimately serves to determine the ensuing treatment of a patient's health care needs.For many patients in acute clinical care, this treatment will begin with a routine pregnancy test.Leaning on theory from Fricker's 2007 book Epistemic Injustice, this thesis explores both the ethical harms implicit in existing pregnancy testing practices, as well as the harms to the effective operation of acute care settings.Routine pregnancy testing is found to be defended in large part through culturally prevalent sex stereotypes and inordinate concern for teratogenicity from anaesthetics.Moreover, according to data gathered in a cross-sectional online research survey, such testing appears to be highly associated with a negative health care experience and impact on patient trust, revealing trends in non-consensual testing and poor protection of patient privacy.This thesis further explores several direct consequences of routine testing on the operational efficiency of acute clinical care spaces, including prolonged wait times in acute clinical care, implications for patient privacy, excessive financial costs to the healthcare institution, and a negative impact on patient trust.Ultimately, this thesis finds that routine pregnancy testing is not compatible with the best interests of patients and makes several recommendations aimed at promoting patient wellbeing in areas such as autonomy, informed consent, privacy, and trust.elevated this project substantially.I would also like to thank my supervisor Professor Phoebe Friesen, who helped me explore the earliest iterations of this project and whose encouragement drove me to pursue it to completion.This thesis would not be complete without the valuable insight and suggestions that she provided.In addition, I owe a debt of gratitude to those who offered invaluable and thoughtful input throughout my degree, especially: Jonathan Alter, Kim Echlin, Professor Ross Upshur, Tyler Paetkau, and finally, Professor Gail Van Norman, whose tireless work on this matter continues to inspire my own.I give great thanks to the many organizations who supported this research by sharing it with potential study participants.These organizations include the Abortion Rights Coalition of Canada, Action Canada SHR, the Femmes du Monde Women's Centre, the Y des Femmes de Montreal (YWCA), Monthly Dignity, the SHORE Centre of Waterloo, the North Shore Women's Centre in BC, the CHEW project YEG
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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 | 0.074 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.016 |
| 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".