Nursing Services Delivery Theory: an open system approach
Bibliographic record
Abstract
AIM: This paper is a discussion of the derivation of the Nursing Services Delivery Theory from the application of open system theory to large-scale organizations. BACKGROUND: The underlying mechanisms by which staffing indicators influence outcomes remain under-theorized and unmeasured, resulting in a 'black box' that masks the nature and organization of nursing work. Theory linking nursing work, staffing, work environments, and outcomes in different settings is urgently needed to inform management decisions about the allocation of nurse staffing resources in organizations. DATA SOURCES: A search of CINAHL and Business Source Premier for the years 1980-2008 was conducted using the following terms: theory, models, organization, organizational structure, management, administration, nursing units, and nursing. Seminal works were included. DISCUSSION: The healthcare organization is conceptualized as an open system characterized by energy transformation, a dynamic steady state, negative entropy, event cycles, negative feedback, differentiation, integration and coordination, and equifinality. The Nursing Services Delivery Theory proposes that input, throughput, and output factors interact dynamically to influence the global work demands placed on nursing work groups at the point of care in production subsystems. IMPLICATIONS FOR NURSING: THE Nursing Services Delivery Theory can be applied to varied settings, cultures, and countries and supports the study of multi-level phenomena and cross-level effects. CONCLUSION: The Nursing Services Delivery Theory gives a relational structure for reconciling disparate streams of research related to nursing work, staffing, and work environments. The theory can guide future research and the management of nursing services in large-scale healthcare organizations.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".