Understanding Determinants of Home Dialysis Use in Canada: A Mixed-Methods Study
Bibliographic record
Abstract
This thesis consists of three related studies presented as three separate manuscripts. The first two comprise part of a larger sequential mixed-methods study with a qualitative and subsequent quantitative (survey) component. The overarching goal of this study was to understand the factors that influence the use of home dialysis, from the perspectives of Canadian nephrologists. The third study was a methodological study (a clinical trial) embedded in the survey, evaluating a novel strategy to incentivize survey responses. In the first paper, we aimed to develop a theoretical framework describing determinants of dialysis modality choice. We selected informants using a maximum-variation sampling strategy, and used in-depth interviews to explore their perspectives. We used a grounded theory-informed analytical approach to construct a taxonomy of barriers and related facilitators to home dialysis use. We triangulated our findings against related published studies and qualitative results from our survey study. This study informed the development of the questionnaire that is the focus of the second study. The second paper describes the development, administration, and results of a 47-item survey measuring Canadian nephrologist perspectives on the relevance of barriers to home dialysis use, and the utility of candidate interventions to overcome them. We used factor analysis to aggregate items into domains, and examined the relationships between respondent and practice characteristics with domain-level scores. Respondents expressed enthusiasm and reluctance towards a number of strategies to optimize home dialysis use. Our findings will guide policy development and further research directed at managing barriers to home dialysis use. The third and final study tests the effectiveness of a promised donation as an incentive for survey completion. We randomized survey recipients to receive standard notifications versus notifications that offered a charitable donation of $40 CAD to the Kidney Foundation of Canada in exchange for returning a completed survey. Contrary to our hypothesis, the intervention was not effective, thus adding to the cumulative evidence that such incentives do not impact on physician response rates.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".