Bibliographic record
Abstract
BACKGROUND: Despite widespread belief in the importance of patient-centred care, it remains difficult to create a system in which all groups work together for the good of the patient. Part of the problem may be that the issue of patient-centred care itself can be used to prosecute intergroup conflict. OBJECTIVE: This qualitative study of texts examined the presence and nature of intergroup language within the discourse on patient-centred care. METHODS: A systematic SCOPUS and Google search identified 85 peer-reviewed and grey literature reports that engaged with the concept of patient-centred care. Discourse analysis, informed by the social identity approach, examined how writers defined and portrayed various groups. RESULTS: Managers, physicians and nurses all used the discourse of patient-centred care to imply that their own group was patient centred while other group(s) were not. Patient organizations tended to downplay or even deny the role of managers and providers in promoting patient centredness, and some used the concept to advocate for controversial health policies. Intergroup themes were even more obvious in the rhetoric of political groups across the ideological spectrum. In contrast to accounts that juxtaposed in-groups and out-groups, those from reportedly patient-centred organizations defined a 'mosaic' in-group that encompassed managers, providers and patients. CONCLUSION: The seemingly benign concept of patient-centred care can easily become a weapon on an intergroup battlefield. Understanding this dimension may help organizations resolve the intergroup tensions that prevent collective achievement of a patient-centred system.
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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".