Canada’s Indigenous Social Workers’ Medicinal Plant Knowledge: A Land-Based Way of Knowing for Indigenous Practice
Bibliographic record
Abstract
Skilled nursing facilities (SNFs) in New Jersey, which provide both short-term rehabilitation and long-term residential care, are faced with mounting pressure due to mandated staffing ratios for certified nursing assistants (CNAs). The purpose and review question for this integrative review centered on best practices and strategies for nursing home leaders to meet the new Centers for Medicare & Medicaid Services and New Jersey standards for CNA staffing ratios in nursing homes. This review used the systems theory to look at SNFs as connected networks where staffing, leadership, finances, and care delivery all work together. An examination of the literature published within the past 5 years identified over 100 relevant articles. From these articles, 25 empirical and nonempirical articles were selected for further analysis using the Johns Hopkins evidence-based practice model and seven major themes and 14 subthemes emerged. The major themes included enhance patient-centered care, implement quality care standards, remove policy barriers, strengthen recruitment and retention, improve communication through leadership support, develop teamwork strategies, and education programs that enhance career paths. An analysis of the themes resulted in six recommendations to improve CNA compliance in New Jersey nursing homes. This led to a five-phase plan aimed at turning around struggling NJ nursing homes. The plan focuses on assessing internal operations, strengthening leadership and staff development, policy adaptation, improving communication, and building long-term stability through continuous feedback and reinvestment. These recommendations can strengthen staff retention and ensure the delivery of high-quality care to New Jersey’s vulnerable elder population by implementing evidence-based strategies, thereby emphasizing positive social change.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".