Changing relationships: how does patient involvement transform professional identity? An ethnographic study
Bibliographic record
Abstract
OBJECTIVES: To understand identity tensions experienced by health professionals when patient partners join a quality improvement committee. DESIGN: Qualitative ethnographic study based on participatory observation. SETTING: An interdisciplinary quality improvement committee of a Canadian urban academic family medicine clinic with little previous experience in patient partnership. PARTICIPANTS: Two patient partners, seven health professionals (two family physicians, two residents, one pharmacist, one nurse clinician and one nurse practitioner) and three members of the administrative team. DATA COLLECTION: Data collection included compiled participatory observations, logbook notes and semi-structured interviews, collected between the summer of 2017 to the summer of 2019. DATA ANALYSIS: Ghadiri's identity threats theoretical framework was used to analyse qualitative material and to develop conceptualising categories, using QDA Miner software (V.5.0). RESULTS: All professionals with a clinical care role and patient partners (n=9) accepted to participate in the ethnographic study and semi-structured interviews (RR=100%). Transforming the 'caregiver-patient' relationship into a 'colleague-colleague' relationship generated identity upheavals among professionals. Identity tensions included competing ideals of the 'good professional', challenges to the impermeability of the patient and professional categories, the interweaving of symbols associated with one or the other of these identities, and the inner balance between the roles of caregiver and colleague. CONCLUSION: This research provides a new perspective on understanding how working in partnership with patients transform health professionals' identity. When they are called to work with patients outside of a simple therapeutic relationship, health professionals may feel tensions between their identity as caregivers and their identity as colleague. This allows us to better understand some underlying tensions elicited by the arrival of different patient engagement initiatives (eg, professionals' resistance to working with patients, patients' status and remuneration, professionals' concerns toward patient 'representativeness'). Partnership with patients imply the construction of a new relational framework, flexible and dynamic, that takes into account this coexistence of identities.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".