Where and how does fundamental care fit within seminal nursing theories: A narrative review and synthesis of key nursing concepts
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To elucidate the synergies between fundamental care and seminal nursing theories. BACKGROUND: Nursing theories are often criticised for their limited clinical relevance, with the existence of a theory-to-practice gap widely acknowledged. Pervasive examples of poor-quality care, particularly for people's most fundamental needs, raise questions as to whether nursing theories sufficiently prioritise fundamental care. The Fundamentals of Care Framework (hereinafter "the Framework") represents a valid, comprehensive and evidence-based description of fundamental care. The Framework captures the complexity and multidimensionality of fundamental care delivery, predicated on the nurse-patient relationship; integration of physical, psychosocial and relational needs; and a supportive context. Despite strong face validity, the Framework's alignment with seminal nursing theories remains unexplored. DESIGN: Narrative review. METHOD: Twenty-nine seminal nursing theories were included. Categories for analysis were developed inductively and deductively, focusing on the themes of relationship, integration of care, context and the theories' ease of use. Results are reported in accordance with PRISMA-ScR guidelines. RESULTS: Though relationship, integration of care and context and were features shared across a number of nursing theories, no single theory depicts these collectively to the same extent as the Framework. In particular, integration of physical, psychosocial and relational aspects of care was found to be poorly described in the theories. CONCLUSION: Failure to account for integration of care means that nursing theories continue to conceptualise fundamental care as a series of discrete tasks. To ensure relevance at the point of care, future nursing theories must accurately reflect the complexities of fundamental care delivery, specifically the need to integrate multiple care needs simultaneously, alongside being straightforward to apply in practice. RELEVANCE TO CLINICAL PRACTICE: Bridging the theory-to-practice gap requires a nursing discourse that is relevant at the point of care. We provide suggestions for how future nursing theories can bridge this gap.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".