Motives for sharing illness experiences on Twitter: conversations of parents with children diagnosed with cancer
Bibliographic record
Abstract
A patient- and family-centred approach in paediatric health care is important because parents are involved in making key decisions about their child’s health care and advocating for the best interest of the child. Parents and family members are increasingly turning to the internet to find and actively share information about their child’s health care. Twitter is one of many online platforms used by parents of children diagnosed with cancer to share information related to their child’s cancer experience. Existing research suggests that there is a need to better understand the motives for using Twitter for sharing content about a child’s cancer experience. Furthermore, there is a lack of theoretical frameworks for characterizing those motives. In this paper, we identify key themes of tweets posted by parents of children diagnosed with cancer and align those themes with motives inspired by the well-studied Everyday Life Information Seeking framework. We propose a new motive in addition to those associated with the framework and suggest that information can be shared for endogenous reasons as well as to meet the needs of others. This paper contributes an increased understanding of motives for sharing information about a child’s cancer journey and extends a theoretical framework for building further knowledge in this area.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 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".