“I Get It, I’m Sick Too”: An Autoethnographic Study of One Researcher/Practitioner/Patient With Chronic Illness
Bibliographic record
Abstract
This autoethnographic research utilizes reflexivity as a method to explore my self-experience of Lyme disease while holding co-occurring identities as a researcher, health professional, and mother. Awareness of self is central in psychotherapy so that therapists do not adversely impact their clients. This is similar for researchers who are ethically required to acknowledge and reduce any potential risk(s) of harm to their participants. In this study, I describe and systematically analyze my experiences as a patient with symptom-persistent Lyme disease, contextualized through co-occurring identities as a mother, a regulated (mental) health professional, and a scholar investigating the embodied experience of being a Lyme disease patient in the Canadian context. The central research question guiding this study is: "What are my experiences with symptom-persistent Lyme disease?" The results of this study suggest reflexivity is an important practice in both health research and healthcare. Relationships with health professionals have a significant impact on patients' healthcare experiences, and engaging in reflexive practice may improve the responsivity of healthcare professionals toward patients' needs and embodied experiences and serve as a check on pre-existing power relations in healthcare. Further, this research contributes to the current academic knowledge on symptom-persistent Lyme disease by offering a reflexive representation of my experiences as a researcher who is also a health professional and a patient within the Canadian healthcare system. Representations of patients' experiences are critical in advancing health research and ensuring equitable care for patients. Autoethnography offers important insights into patients' disease experiences.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".