Listening to voices that are challenging to hear: participatory hermeneutics ethnography with children with medical complexity
Bibliographic record
Abstract
This manuscript explores the integration of participatory hermeneutic ethnography in research with children with complexity, particularly those who communicate differently. Traditional research methods often exclude these children, leading to a lack of representation of their unique experiences. The study employs a participatory approach that emphasizes ethical considerations, relational perspectives, and the inclusion of non-verbal communication methods to better capture the voices of these children. The research involved eight children with medical complexity, along with their families and healthcare professionals, providing a comprehensive understanding of their lived experiences. Data collection was conducted over seven months using methods such as participant observation, informal and structured interviews, and innovative techniques like drawing, storytelling, and play-based activities. The study highlighted the importance of respecting each child’s unique communication style and ensuring their active participation in the research process. The findings reveal that traditional biomedical approaches often overlook the complex social and moral realities of children’s experiences. By employing a hermeneutic framework, the research provided deeper insights into the children’s expressions, both verbal and non-verbal, within their broader social contexts. This approach also underscored the significance of understanding children’s voices through the lens of their relational and social environments. The study underscores the necessity of inclusive and participatory research methodologies for effectively capturing the varied experiences of children with medical complexity. It calls for a shift away from normalized expectations of verbal communication and emphasizes the need for continued development of methods that respect and validate the agency of all children, regardless of their communication abilities. The implications of this research extend to both academic inquiry and clinical practice, advocating for more ethically attuned and inclusive approaches in working with children who communicate differently. Future research should build on these findings, exploring innovative strategies to further empower these children and enhance our understanding of their experiences.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".