Neglecting the Importance of the Decision Making and Care Regimes of Personal Support Workers: A Critique of Standardization of Care Planning Through the RAI/MDS
Bibliographic record
Abstract
PURPOSE: The Resident Assessment Instrument-Minimum Data Set (RAI/MDS) is an interdisciplinary standardized process that informs care plan development in nursing homes. This standardized process has failed to consistently result in individualized care planning, which may suggest problems with content and planning integrity. We examined the decision making and care practices of personal support workers (PSWs) in relation to the RAI/MDS standardized process. DESIGN AND METHODS: This qualitative study utilized focus groups and semi-structured interviews with PSWs (n = 26) and supervisors (n = 9) in two nursing homes in central Canada. RESULTS: PSWs evidenced unique occupational contributions to assessment via proximal familiarity and biographical information as well as to individualizing care by empathetically linking their own bodily experiences and forging bonds of fictive kinship with residents. These contributions were neither captured by RAI/MDS categories nor relayed to the interdisciplinary team. Causal factors for PSW exclusion included computerized records, low status, and poor interprofessional collaboration. Intraprofessional collaboration by PSWs aimed to compensate for exclusion and to individualize care. IMPLICATIONS: Exclusive institutional reliance on the RAI/MDS undermines quality care because it fails to capture residents' preferences and excludes input by PSWs. Recommendations include incorporating PSW knowledge in care planning and documentation and examining PSWs' nascent occupational identity and their role as interprofessional brokers in long-term 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 | 0.195 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.017 | 0.138 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.001 | 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".