Transforming normative, ableist, and biomedical orientations to living well and quality of life in nursing: Reimagining what a ventilated body can do
Bibliographic record
Abstract
A goal of living as well as possible is central to practice and research with young adults living with home mechanical ventilation (HMV). Significant effort has been put into conceptualizing and measuring the quality of life (QOL) as a proxy for living well. Yet, dominant understandings of QOL have been influenced by normative, ableist, and biomedical discourses about what constitutes a good life that, when applied in practice and systems with those living with HMV, can contribute to exclusion and constrain opportunities to live well. Inquiry into what certain understandings of living well can do is critical to opening up possibilities to reimagine living well with HMV. This paper draws on findings from a critical narrative inquiry that explored the experiences of five young adults (ages 18-40 years) living with HMV. Data were co-constructed virtually through an initial interview and photo-elicitation using participant-generated photographs. A critical narrative analysis of participants' stories made visible the ideological effects of ableist, biomedical, and individualist discourses and how the young adults reproduced and resisted these dominant discourses. Their stories further opened up possibilities for nurses and other healthcare providers to see living well and QOL differently.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".