Experience of Care – Furthering the Patient Experience Agenda
Bibliographic record
Abstract
The measurement of the patient experience is a global movement that has caught the attention of healthcare reformers. The use of patient experience data to ameliorate healthcare practice is promising, although standardization in what, where, how and whose experience is measured does not yet exist. To truly further the patient experience agenda, there needs to be adoption at the system, regional and local level to help promote, inspire and lead to sustainable change. Caregiver insight into the patient experience should be leveraged to learn what is important to patients and extract more useful data, as they are often present during transitions in care that span across the continuum. Embracing the voice of the patient as part of the process to improve quality, outcomes and experience will no doubt lead to impactful change and better care.
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 | 0.077 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.025 | 0.045 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 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".