A qualitative study exploring the lived experiences of deconditioning in hospital in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Older adults, especially those with physical and social complexities are at risk of hospital-associated deconditioning. Hospital-associated deconditioning is linked to increased length of stay in hospital, stress, and readmission rates. To date, there is a paucity of research on the experiences and implications of deconditioning in hospital from different perspectives. Therefore, the objectives of this exploratory, descriptive qualitative study were to explore hospital-associated deconditioning from the views of different stakeholders and to develop an understanding of deconditioning from physical, social, and cognitive perspectives. METHODS: Between August 2018 and July 2019, in-depth, semi-structured interviews were conducted with patients 50 years or older, who had a hip fracture or delay in discharge, as well as caregivers, providers, and decision-makers who provided support or impacted care processes for these patients. Participants were recruited from one urban and one rural health region located in Ontario, Canada. All interviews were audio-recorded, transcribed, and analyzed using a constant comparison approach. RESULTS: A total of 80 individuals participated in this study. Participants described insufficient activities in hospital leading to boredom and mental and physical deconditioning. Patients were frustrated with experiencing deconditioning and their decline in function seemed to impact their sense of self and identity. Deconditioning had substantive impacts on patients' ability to leave hospital to their next point of care. Providers and decision-makers understood the potential for deconditioning but felt constrained by factors beyond their control. Factors that appeared to impact deconditioning included the hospital's built environment and social capital resources (e.g., family, roommates, volunteers, staff). CONCLUSIONS: Participants described a substantial lack of physical, cognitive, and social activities, which led to deconditioning. Recommendations to address deconditioning include: (1) measuring physical/psychological function and well-being throughout hospitalization; (2) redesigning hospital environments (e.g., create social spaces); and (3) increasing access to rehabilitation during acute hospital stays, while patients wait for the next point-of-care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".