Disavowal of the Behaviorist Paradigm in Nursing Education: What Makes It So Difficult To Unseat?
Bibliographic record
Abstract
The literature is replete with calls for disavowal of the behaviorist paradigm in nursing education, a paradigm charged with producing successive generations of passive learners who are incapable of instigating much needed and long overdue reforms within the health care system. Only rarely has this call been challenged by nurse educators. In this article, the role of a paradigm in delineating the nature of and solutions to significant problems within a scientific community is explored. I posit that the current eschewal of behaviorism by nurse educators stems not from its failure to solve significant problems in nursing education but rather from an apparent shift in value orientation--a shift from effecting learning (and health) outcomes to effecting social change. Despite this seeming shift in values, effecting learning outcomes is still held to be an essential aspect of nursing education, and it is because of this, I argue, that the behaviorist paradigm is so difficult to unseat.
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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 | 0.044 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.108 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".