Relating person‐centredness to quality‐of‐life assessments and patient‐reported outcomes in healthcare: A critical theoretical discussion
Bibliographic record
Abstract
Engagement with the historical and theoretical underpinnings of measuring quality of life (QoL) and patient-reported outcomes (PROs) in healthcare is important. Ideas and values that shape such practices-and in the endgame, people's lives-might otherwise remain unexamined, be taken for granted or even essentialized. Our aim is to explicate and theoretically discuss the philosophical tenets underlying the practices of QoL assessment and PRO measurement in relation to the notion of person-centredness. First, we engage with the late-modern history of the concept of QoL and the act of assessing and measuring it. Working with the historical method of genealogy, we describe the development of both QoL assessments and PRO measures (PROMs) within healthcare by accounting for the contextual conditions for their possibility. In this way, the historical and philosophical underpinnings of these measurement practices are highlighted. We move on to analyse theoretical and philosophical underpinnings regarding the use of PROMs and QoL assessments in clinical practice, as demonstrated in review studies thereof. Finally, we offer a critical analysis regarding the state of theory in the literature and conclude that, although improved person-centredness is an implied driver of QoL assessments and PROMs in clinical practice, enhanced theoretical underpinning of the development of QoL assessments is called for.
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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 | 0.096 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.105 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.012 | 0.026 |
| 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".