Being bedridden was a slow process influenced by interactions with the environment, nurses, and social tiesCommentary
Bibliographic record
Abstract
A Zegelin Dr A Zegelin, Private University Witten/Herdecke, Witten, Germany; zegelin@uni-wh.de What are the experiences of people who become bedridden? Grounded theory. Nursing homes and personal residences in Witten, Germany. 32 people who were 62–98 years of age (59% women), had chronic illnesses, were bedridden for 2 weeks to 4 years, and had had the option during this time of being able to get up. 30-minute semistructured interviews were recorded, transcribed, and analysed using a grounded theory approach. Phase 1: instability. All patients reported problems while walking before they became immobile (eg, “shaky,” “dizzy”). Some used a walking stick or a rollator when leaving the house. Most patients only moved around inside the house, supporting themselves on furniture, and all had fallen at some stage. Phase 2: incident. All patients had a hospital stay and/or a fall, which reduced their mobility. Patients remained passive and withdrew to their beds, not knowing where else to go. Transfer situation . How patients were transferred affected how often they got up. If carers seemed weak, incompetent, or complaining or if 2 carers were needed to make the transfer, patients reduced their demands. …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".