Power of narrative: a case study about documenting private insightful experiences while dealing with pain and associated disability
Bibliographic record
Abstract
Objective: People adjusting to living with a chronic disability, such as chronic pain, seek support and resources from societal systems, including health systems, to help them cope with this reality. This case study describes the use of a digital health platform designed to help in that quest. Method: MyHealthMyRecord (MHMR), is being developed to record, register and curate personal private experiences of a chronic condition. MHMR allows users to record and log short (30-90s) personal and private audio-videos of their accommodation-seeking journey in a way that can be encrypted, registered, curated and shared privately. This case study describes the use of a prototype version of the platform by a participant co-designer who experienced a sudden onset of a chronic pain condition, of undetermined origin. System use began three months after the onset of the condition and just after being discharged from several months of hospitalization without any definitive diagnosis. Result: During a three-month period, 65 short unstructured contributions were authored and logged. This paper presents a qualitative analysis of that content. The clips used various communication styles that documented experiences, concerns, issues, positive and negative interactions and pain episodes. Using thematic analysis with open coding, three domains (person-facing, accessibility and system-facing) and eight themes (pain, joy, therapy, environmental, recommendations, technical, culture and communication) were identified. Comments about pain, stress, etc., were the most common and occurred in 75% of all videos while technical and therapy/physio related comments were the fewest and occurred in 3 and 9% of the videos, respectively. Conclusion: We conclude that it is possible to create recordings of events, thoughts, reflections and issues on different aspects affecting an individual's health and well-being impact, including effects of the chronic condition as well as tangential outcomes such as accessibility (or lack of it), using MHMR over a longer period of time. The next steps will be to develop functionality to annotate the recordings, automatically analyze and summarize collections of recordings to make them consumable, useful and understandable to the individual and others, and then to share those analyses and summaries with others. In addition, evaluate this functionality longitudinally with more users.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".