Patient-Guided Tours: A Patient-Centered Methodology to Understand Patient Experiences of Health Care
Bibliographic record
Abstract
PURPOSE: The experience-based design approach using patient-guided tours (PGT) has been suggested as an effective way to understand the patient experience and may better allow the patient to recall thoughts and feelings. The objective of this study was to assess how patients with a disability perceive the effectiveness of PGTs for understanding their experiences of receiving primary health care. METHODS: A qualitative study design was used. Participants were chosen by convenience sampling. The patient was asked to walk through the clinic as they would on a "typical visit" while describing their experiences. They were questioned about their experience and perception of PGTs. The tour was audiotaped and transcribed. The investigators took field notes and completed thematic content analysis. RESULTS: Eighteen patients participated. The main findings were: (1) Touchpoints and physical cues were effective in eliciting experiences that participants stated they would not have recalled using other research methods, (2) The ability for participants to show the investigator aspects of the space that impacted their experience enabled the investigator to "see through their eyes" resulting in ease of communication and a sense of empowerment, (3) PGTs encouraged individuals to be active participants which fostered comfort and collaboration, and (4) PGTs may exclude those that are severely disabled. CONCLUSION: This method was perceived as effective at eliciting experiences of patients with a disability. It has benefits over more traditional research methods by allowing the participant to refresh their memory at "touchpoints" and enabling them to be active participants.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".