Patient engagement in healthcare planning and evaluation: A call for social justice
Bibliographic record
Abstract
Patient engagement in healthcare planning and evaluation has been promoted as a way to improve healthcare's ability to meet patients' needs. However, populations experiencing oppression and discrimination, such as racism, colonialism, sexism, heterosexism, cisnormativity, ableism, classism, and poverty, are often underrepresented in patient engagement spaces. The context and structure of patient engagement processes may systematically exclude certain populations from participating in meaningful ways or from participating at all. By excluding certain populations from active, meaningful patient engagement, we risk planning and evaluating health services on the basis of the values, needs, and preferences of the dominant population. This, in turn, will further entrench health inequities and preclude the ability to surface ideas that challenge dominant conceptualisations of health and healthcare, thereby reinforcing the status quo rather than promoting healthcare transformation. Recognising that experiences of health, healthcare, and patient engagement processes are mediated through gender, race, ability, sexual orientation, and other dimensions of diversity, it is proposed that processes for engaging patients in healthcare planning and evaluation must by intersectional, attend to systemic and power relations, and truly put patients in the driver's seat of engagement processes. Health services planners and evaluators need to create more inclusive, accessible, and appropriate patient engagement experiences in order to focus on transforming healthcare towards a more socially just system.
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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 | 0.465 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.035 | 0.114 |
| Scholarly communication | 0.058 | 0.048 |
| Open science | 0.011 | 0.076 |
| Research integrity | 0.029 | 0.058 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".