Finding the fundamental needs behind resistance to care: Using the Fundamentals of Care Practice Process
Bibliographic record
Abstract
A person living with Alzheimer's disease (PA) can experience difficulty during bodily care and therefore may show resistance to care behaviours (RTCBs). Nurses must take a clinical approach to planning care that meets the person's needs. Therefore, it is necessary to identify training strategies for bedside nurses and nursing students. AIMS AND OBJECTIVES: To describe and discuss how the FOC practice process (FOC-PP) can help nurses understand PAs who show RTCBs during bodily care. BACKGROUND: Resistance to care behaviour phenomenon and the importance of bodily care as fundamental care are described. The FOC-PP enables nurses to apply the FOC framework in their practice. DESIGN: This discursive paper is based on the literature of the FOC framework and PP. METHOD: A clinical scenario that develops through the five stages of the FOC-PP. RESULTS: The scenario centres on Mrs. Emily Morgan, 81, who lives in a nursing home and is not receiving the bodily care that she needs. Camille, a nursing student, and her supervisor Florence collaborate with Mrs. Morgan's family to improve the quality of her care. Three particular aspects of nursing practice based on the FOC-PP are described: the critical thinking process, relational process and pedagogical process. CONCLUSION: The FOC-PP promotes holistic care centred on the person and his or her needs and encourages the nurse to use his or her skills and knowledge. All these dimensions are fundamental for high-quality nursing care. RELEVANCE TO CLINICAL PRACTICE: Mrs. Morgan's scenario enables us to perceive that the FOC-PP is very useful for nursing students and bedside nurses. However, given the amount of specific and diverse knowledge required by the FOC-PP, it is necessary to identify avenues for teaching them. Using clinical scenarios could facilitate the integration of the FOC-PP, with taking into account the specific characteristics of individual clients.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".