Exploring the reassessment of palliative home care clients: A mixed methods study
Bibliographic record
Abstract
Introduction: The timely reassessment of clients receiving palliative home care (HC) is critical for supporting clinicians with care planning and service delivery. However, there is little information on the frequency of reassessments and factors driving reassessment in palliative HC clients. Objectives: The objectives of this study were to: (i) determine the proportion of palliative HC clients reassessed with the interRAI Palliative Care (interRAI PC) instrument, (ii) calculate the average interRAI PC reassessment interval, (iii) identify the key factors driving interRAI PC reassessment at various intervals, and (iv) explore the factors that influence palliative HC clinicians to reassess their clients in general. Methods: This sequential mixed methods study was comprised of three phases: a quantitative phase (i.e., Phase I), a qualitative phase (i.e., Phase II), and a supplementary quantitative phase (i.e., Phase III). Phase I was a retrospective cohort study using secondary interRAI PC assessment data for palliative HC clients assessed in Ontario from 2011 to 2022 (n = 128,740). Clinically meaningful differences between clients who were not reassessed and those reassessed at four intervals (i.e., within 90 days, 91-180 days, 181-365 days, and beyond 365 days) were identified using absolute standardized differences. A standardized difference of 0.2 or greater represented at least a small effect size and was considered to denote a significant difference. Phase II followed a collective exploratory case approach with palliative HC clinicians (i.e., nurses and nurse practitioners) being recruited from two provinces in Canada (n = 3). Clinicians were recruited through a snowball convenience sampling approach. Background surveys and semi-structured interviews were conducted to gain a deeper understanding of the factors that influence their decision to complete reassessments. The interviews were transcribed verbatim and analyzed using a cross-case synthesis and Braun & Clarke’s steps for reflexive thematic analysis. Phase III followed the same design as the first phase of the study and was used to address additional predictors of interRAI PC reassessment based on the results of Phase II. Results: In Phase I, only 30.5% of the sample had a recorded reassessment, with the average reassessment interval being 198 days (standard deviation = 156). Across all comparisons, clients were significantly more likely to be reassessed if they had a prognosis of 6 months or longer, no/moderate health instability, no/mild levels of functional impairment, and/or a low/mild risk of developing a pressure ulcer. In Phase II, the researcher generated two main themes through her analysis: individualized care plans and a connected care team. In Phase III, clients with independent locomotion (i.e., walking or wheeling) were more likely to receive a reassessment at any interval. Conclusion: The timely reassessment of palliative HC clients is critical to ensure their changing care needs are identified and they are receiving the proper supports and resources. While this work identified several factors influencing reassessment, further research needs to be conducted to gain a deeper understanding of how the different predictors interact with one another.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".