Patient, consumer, client, or customer: what do people want to be called?
Bibliographic record
Abstract
OBJECTIVE: To clarify preferred labels for people receiving health care. BACKGROUND: The proper label to describe people receiving care has evoked considerable debate among providers and bio-ethicists, but there is little evidence as to the preferences of the people involved. DESIGN: We analysed dictionary definitions as to the derivation and connotations of such potential labels as: patient, client, customer, consumer, partner and survivor. We then surveyed outpatients from four clinical populations in Ontario, Canada about their feelings about these labels. SETTING AND PARTICIPANTS: People from breast cancer (n = 202), prostate disease (n = 202) and fracture (n = 202) clinics in an urban Canadian teaching hospital (Sharpe study), and people with HIV/AIDS at 10 specialty care clinics and three primary care practices affiliated with the HIV Ontario Observational Database (n = 431). VARIABLES AND OUTCOME MEASURES: The survey instruments included questions about opinion of label, role in treatment decision-making (the Problem Solving Decision Making scale), trust, use of information and health status. RESULTS: Our respondents moderately liked the label 'patient'. The other alternatives evoked moderate to strong dislike. CONCLUSIONS: Many alternatives to 'patient' incorporate assumptions (e.g. a market relationship) which care recipients may also find objectionable. People who are receiving care find the label 'patient' much less objectionable than the alternatives that have been suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".