Bibliographic record
Abstract
PURPOSE: Mental health nursing is not the same as psychiatry, so it is important for nurses to have an understanding of the defining attributes, antecedents, consequences, model cases, and empirical referents of post-traumatic stress disorder (PTSD). METHOD: Walker and Avant's (2005) method is used to guide this concept analysis of PTSD. FINDINGS: Four attributes arise from this concept analysis, which are addressed through both the DSM-IV and DSM-5 (American Psychiatric Association, /): triggering event or events, re-experiencing, fear, and helplessness. Though a majority of the defining attributes are addressed through both versions of the DSM, a key fifth attribute arises through this concept analysis: a disruption of meaning. CONCLUSIONS: A better understanding of PTSD from a nursing perspective will help inform appropriate nursing interventions and prevention strategies, while expanding the knowledge synthesis and contribution of the nursing profession. PRACTICE IMPLICATIONS: A model case, borderline case, and contrary case of PTSD are provided. Discussion of the importance of a lack or loss of meaning in PTSD is included, along with exploration of transformative learning theory to inform clinical practice for nurses addressing a disruption of meaning.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".