Participant-Generated Timelines: A Participatory Tool to Explore Young People With Chronic Pain and Parents’ Narratives of Their Healthcare Experiences
Bibliographic record
Abstract
Visual methods are becoming more evident in health research. Timeline drawings have been used as a participatory tool alongside interviews in life course research. In this article, we describe how a method involving timeline generation can explore patient experiences along a treatment continuum. Grounded in previously published evidence and using specific examples from two studies exploring the experiences of young people treated for chronic pain, we outline the key components of this method. Moreover, we highlight the flexibility of its application and the importance of using a person-centered approach in tailoring the application pragmatically to study population-specific needs and characteristics, while answering the research question. We also reflect on how the dynamic visual display of the timeline and participants' explanations add perspective and understanding to complex and multidimensional human experiences associated with healthcare treatment. Furthermore, we outline how this method can help capture changes in the meaning and sense-making of these experiences over time, all the while fostering empowerment in study participants. Finally, the key considerations of using the method are outlined. It is our aim that this article provides the details required to inspire others to consider this novel method as a means of capturing the healthcare experiences of young people with other chronic conditions, an important first step in fostering the changes required to improve the quality of healthcare services and research.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".