Using Patient Blogs on Social Media to Assess the Content Validity of Patient-Reported Outcome Measures: Qualitative Analysis of Patient-Written Blogs
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are questionnaires that measure patient outcomes related to quality of life, health, and functioning, and are increasingly used to assess important outcomes from the patient's perspective. For PROMs to contribute to better health and better care, it is vital that their content validity be adequate. This requires patient involvement in various steps of PROM development. PROM developers not only recognize the benefits of patient involvement but also report difficulties in recruiting patients and experience patient involvement as time-consuming, logistically challenging, and expensive. OBJECTIVE: This study seeks to explore different strategies for disclosing the experiential knowledge of patients, namely through analyzing patient stories on the web and social media. The research questions are as follows: (1) how do bloggers living with a disease experience their health-related quality of life? (2) How are these experiences reflected in the domains and items of PROMs related to their disease? METHODS: First, a qualitative analysis of blogs written by patients was performed. Second, subthemes and underlying codes resulting from this qualitative analysis were systematically compared with the domains and items in PROMs for the respective diseases that the bloggers write about. Blogs were identified via the Google search engine between December 2019 and May 2021. RESULTS: Bloggers describe a wide range of experiences regarding their physical functioning and health; mental well-being; social network and support; daily life, education, work, and leisure; coping; and self-management. Bloggers also write about their positive and negative experiences with health care delivery, the organization of health care, and health care professionals. In general, patients' experiences as described in blogs were reflected in the domains and items of the PROMs related to their disease. However, except for diabetes mellitus, in all the sets of PROMs, potentially missing topics could be identified. Similarly, with the exception of Parkinson disease, all PROMs address issues that patients did not write about in their blogs and that might therefore be redundant. CONCLUSIONS: Web-based patient stories in the form of blogs reveal how people living with a certain disease experience their health-related quality of life. These stories enable analyses of patients' experiences that can be used to assess the content validity of PROMs. This can be a useful step for researchers who are looking for sets of measuring instruments that match their purposes.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".