Bibliographic record
Abstract
This study explored the life-world of individuals being treated for end stage renal disease with a home haemodialysis machine (HHDM). A phenomenological framework was employed to gain an understanding of the lived-body in relation to the HHDM in order to assist in the planning of future social work interventions. A purposive sample of three participants who had each experienced various lengths of HHDT, were interviewed using two semi-structured in-depth interviews, one week apart. Interviews were conducted at a place of the participant's choice. Participants were asked to speak about their experience of HHDT and how it related to their bodily experience. The findings were interpreted using interpretive phenomenological analytic methods. The central theme that arose from the data was "Struggles between the body and machine". This theme spoke to paradoxical dilemma of living with a life saving machine that you have no control over. Implications suggest that the machine's tendency to be personified, means that it be considered as family member in social assessment. Emotional support to patients should acknowledge the machines dehumanizing tendencies and as well as the issue of its personification. Finally, a patients unmet needs may turn to subversion if not addressed by staff, suggesting that a systematic process to empower patients be established.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".