Patients' voices and experiences of "being a patient" on an internal medicine unit in an urban hospital: A qualitative inquiry
Bibliographic record
Abstract
In this qualitative inquiry, I focus on understanding the meanings of "being a patient" on an internal medicine ward in an urban hospital in Montreal, Quebec. I use open-ended, conversational style interviews with four hospitalized patients to understand their diverse patient voices. I define voice as the diverse ways patients express their meanings of lived experiences on this ward. I draw from two social science theorists to frame my inquiry: French phenomenologist Merleau-Ponty and Russian literacy critic, Bakhtin. I also draw from applied researchers Maguire and Cordella, who work in voice-related methodologies. The participants express their preferred voices as patients and the type of voices they preferred their health care providers to use during medical encounters and dialogues. They describe their lived experiences as hospitalized patients, express what they wished they knew before their hospital admission, and provide their recommendations for future hospitalized patients. Their utterances offer implications for medical and health educators, physiotherapists and health care providers, and health care qualitative researchers in listening for and responding to patient voices.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".