Getting to Know Your Patient: Content Analysis of Patients’ Answers to a Questionnaire for Promoting Person-Centered Care
Bibliographic record
Abstract
BACKGROUND: Person-centered care (PCC) encourages patients to actively participate in health care, thus facilitating care that fits the life of the patient. Therefore, health care professionals (HCPs) need to know the patient. As part of a broad policy for improving PCC, a digital questionnaire ("We would like to know you") consisting of 5 questions has previously been developed to help HCPs to get to know the patient with the help of patient and staff involvement. OBJECTIVE: The purpose of this study was to provide insight into the content and aims of the questionnaire to understand its potential and usability. METHODS: We conducted a qualitative, retrospective content analysis of patients' answers using NVivo Pro (QSR International). The questionnaire was used in the outpatient neuro-oncology department of a Dutch academic hospital. RESULTS: Of 374 invited patients, 78 (20.9%) completed the questionnaire. We selected a sample of 42 (54%) of the 78 patients. Patients used a median of 16 (IQR 7-27) words per question, and most answers were easily interpretable. When asked about important activities, social activities, sports, or maintaining a normal life were most frequently mentioned. Patients wrote about fear of the disease, its possible influence on life, or fear of the future in general. Patients wanted HCPs to know about their care and communication preferences or shared personal information. They formulated expectations about effective treatment, communication, and the care process. CONCLUSIONS: The questionnaire seems usable because patients provide interpretable answers that take little time to read, which HCPs can use to personalize care. Our study shows the potential of the questionnaire to help deliver PCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".