Micro-Meso-Macro Practice Tensions in Using Patient-Reported Outcome and Experience Measures in Hospital Palliative Care
Bibliographic record
Abstract
This article applies a micro-meso-macro analytical framework to understand clinicians' experiences and perspectives of using patient-reported outcome and experience measures (PROMs and PREMs) in routine hospital-based palliative care. We structure our discussion through qualitative analysis of a design and implementation project for using an electronic tablet-based tool among hospital-based palliative clinicians to assess patients' and their family caregivers' quality of life concerns and experiences of care. Our analysis identified three categories of practice tensions shaping clinicians' use of PROMs and PREMs in routine care: tensions surrounding implementation, tensions in standardization and quantification, and tensions that arose from scope of practice concerns. Our findings highlight that clinicians necessarily work within the confluence of multiple system priorities, that navigating these priorities can result in irreducible practice tensions, and that awareness of these tensions is a critical consideration when integrating PROMs and PREMs into routine practice.
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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 | 0.156 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".