Harnessing Social Media to Develop Conceptual Domains of Quality of Life for Adolescents With Advanced Cancer
Bibliographic record
Abstract
BACKGROUND: Many adolescents will not survive their cancer diagnosis and will live with advanced cancer (cancer i.e. difficult to cure). Due to the advancement of cancer therapies, many adolescents will live with advanced cancer for long periods of time. Enhancing QoL is a well-established goal of their clinical oncology and palliative care however, there has been little research to conceptualize QoL in ways meaningful to them. There has also been a lack of QoL research focused on the inclusion of their voices and experiences into QoL construct development. OBJECTIVES: The aim of this study was to develop proposed conceptual domains of QoL relevant to adolescents with advanced cancer. METHODS: This study was a qualitative study grounded in Interpretive Description. We used social media content created by adolescents living with advanced cancer to inform the development of QoL domains. Adolescents are increasingly using social media to share their experiences and we believed social media would facilitate access to rich data. RESULTS: 235 social media posts recorded by 14 adolescents were included in the analysis. This analysis generated domains relevant to the QoL of adolescents with advanced cancer: (1) Perceived Health, (2) The Lived Body, (3) Emotional Wellbeing, (4) Normalcy, (5) Purpose and Direction and (6) Re-Orientation. CONCLUSIONS: The QoL of adolescents with advanced cancer is poorly understood. This research has generated unique conceptual domains of QoL relevant to this population of adolescents. These concepts will inform the future development of a patient-reported outcome measure (PROM) that can measure their QoL.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".