Urinary tract infections and urinary bladder health experiences of persons with spinal cord injury in a Canadian province: A mixed methods study showcasing infection prevention as health inequity case
Bibliographic record
Abstract
Urinary tract infections (UTIs) are one of the most frequent secondary complications among people with spinal cord injury (SCI). The prevention and management of UTIs is prioritized by stakeholders across Canada. The purpose of this study was to gain an in-depth understanding of the urinary bladder (bladder) management experiences of people with SCI in Alberta communities, especially how UTIs are experienced and managed. Convergent mixed methods parallel databases variant. Communities across Alberta, Canada. 39 survey participants and 19 interview participants, all with SCI. One-on-one phone semi-structured interviews analyzed using thematic analysis. Quantitative surveys included demographic, multichoice, and Likert Scale questions analyzed using descriptive analysis. Both methods explored people with SCI’s experiences with bladder management and UTIs. Qualitative and quantitative results were integrated through a comparison joint display table and meta-inferences. Qualitative themes and descriptive statistics further integrated as mixed core-statements. Bladder routine is central to daily life and maintaining bladder health, avoiding UTIs, is the priority. Several health inequities are related to (1) financial barriers dictating how bladder is managed, (2) low perceived support for appropriate bladder management, (3) low healthcare access to appropriate UTI management and (4) low providers’ capacity to support bladder management and build trust with persons with SCI. Action is required to address identified health inequities, including improvement of financial support, like appropriate catheter coverage, decrease barriers to access appropriate care and improvement of providers’ capacity to address SCI bladder care.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".