Preferred Terms for Users of Mental Health Services Among Service Providers and Recipients
Bibliographic record
Abstract
OBJECTIVE: The terms used to refer to recipients of psychiatric services continue to be controversial. Terms in current use include "patient," "client," "consumer," and "survivor." In this study mental health service recipients and providers were surveyed about their preferences among these terms, and responses were analyzed to identify factors associated with various preferences. METHODS: A total of 550 service providers and 427 recipients at four sites in Ontario-two provincial psychiatric hospitals, a private mental health center, and a psychiatric unit of a general hospital-participated in a brief anonymous survey. RESULTS: Among service providers, 68.4 percent preferred the term "patient," 26.5 percent preferred "client," and.5 percent preferred "consumer." Logistic regression analysis showed that service providers' preferences were associated with age and gender. Among service recipients, 54.8 percent preferred the term "patient," 28.8 percent preferred "client," 7 percent preferred "survivor," and 2.8 percent preferred "consumer." Service recipients' preferences were associated with site, self-reported diagnosis, and employment status. CONCLUSION: The study results indicate lack of universality in preferences for terms for users of mental health services and suggest the need for dialogue about preferred terms between service providers and recipients.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".