Development of a shared theory in palliative care to enhance nursing competence
Bibliographic record
Abstract
AIM: This discussion article focuses on the theoretical development of a shared theory in the field of palliative care nursing through a process of comparison between Bandura's social cognitive theory and Orem's conceptual model. BACKGROUND: In many countries, nurses are little prepared to provide care to patients with life-limiting illness. Bandura's theory provides an appropriate framework for evaluating the impact of training programmes aimed at improving nursing competence in palliative care. However, this borrowed psychological theory is not specific to nursing contexts. Orem's self-care deficit theory seems to be an appropriate nursing model to guide the use of Bandura's theory in palliative care nursing situations. DATA SOURCES: A review of the literature published between 1987 and 2011 was conducted to evaluate how Bandura's social cognitive theory and Orem's conceptual model have been linked at a theoretical level in the past. DISCUSSION: Bandura's theory has been linked with Orem's model essentially at the patient level. A new shared theory that combines Bandura's social cognitive theory and Orem's conceptual model at the nursing level is thus proposed. Palliative care nursing self-competence is hypothesized to influence the quality of nursing interventions in palliative care situations. CONCLUSION: To further demonstrate the relevance of this proposed shared theory in palliative care nursing contexts, empirical studies are recommended. This shared theory has the potential to provide a solid theoretical framework for evaluating nursing training programmes and, eventually, to improve quality of care and quality of life for patients with life-limiting illness.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".