Beyond experiential knowledge: a classification of patient knowledge
Bibliographic record
Abstract
Abstract Patients’ experiential knowledge is increasingly documented as a valid form of knowledge in the health sector and is often said to be a source of valuable information to complement the knowledge of health professionals. Although this increased recognition is outlined in the health science literature and formalized in certain organizational and clinical contexts, it remains difficult for various actors of the health ecosystem to contour the different forms of knowledge patients acquire through their experience as well as to consider them as essential in co-building care plans and as an asset to build care relationships. The aim of this review is twofold: (1) to challenge the dominant model of knowledge in medicine and healthcare by making the various forms of patient knowledge more explicit and tangible and (2) to provide a better understanding of what experiential knowledge consists of by classifying the various forms of knowledge patient acquire, develop, and mobilize throughout their care journey. A narrative review allows to classify six types of patient knowledge according to their source of learning: embodied, monitoring, navigation, medical, relational, and cultural knowledge. The three main sources of learning, namely the self, the system, and the community grounds patients’ learning process in their health journey.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".