NURSING ASSISTANTS’ USE OF AUTONOMY-SUPPORTIVE STRATEGIES IN LONG-TERM CARE
Bibliographic record
Abstract
Maximizing nursing home (NH) resident autonomy is a person-centered care best practice. At times, resident decisions are based on preferences that some NH staff view as unhealthy or potentially risky. Support of resident autonomy is a fundamental aspect of person-centered care NHs, challenging nursing assistants (NAs) to balance the need to minimize physical risks associated with some residents' preferences with the need to honor resident autonomy. Autonomy-supportive strategies have been investigated in the areas of education, parenting, and psychotherapy, but there have been no studies to date examining how NAs support resident autonomy in NHs. The purpose of this study was to explore autonomy-supportive strategies used by NAs in three NH neighborhoods at a Veterans Affairs Medical Center. Approximately 80 hours of behavioral observation and 13 interviews were conducted with NAs across the three neighborhoods. Data were analyzed using thematic analysis. Ten autonomy-supportive strategies were identified: assisting, monitoring, encouraging, bargaining, informing, providing instructions, persuading, asking, providing options, and redirecting. Although all strategies incorporated some degree of shared decision-making between NAs and residents, some strategies were more restrictive than others. Persuading and redirecting were effective at impeding residents from engaging in risky behaviors, while assisting and encouraging were ideal for promoting functioning independence and freedom. A common theme across all strategies was the use of respectful, non-controlling language. Results from the study contribute to the general literature on autonomy by elucidating the types of strategies NAs can use to promote greater resident autonomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".