Narrative Methods and Children: Theoretical Explanations and Practice Issues
Bibliographic record
Abstract
TOPIC: The Narrative approach is an innovative way of working with children and adolescents experiencing mental health problems. This approach can be effectively integrated with the expressive arts and other nonverbal ways of accessing the life world of children. In addition, the approach promotes respect for and collaboration with the child in working towards healing and growth. PURPOSE: In this paper core features of the narrative approach are described; the theoretical and philosophical and evidence base for this approach as well as its congruence with the special nature and needs of children will be explored. Finally, the benefits and challenges of this approach in relation to a specific clinical situation will be highlighted. SOURCES USED: Published literature and the author's clinical experiences. CONCLUSION: Narrative methods are ideally suited for addressing needs of children experiencing mental health problems and can enhance therapeutic effectiveness. Some of the challenges associated with its use include: finding creative ways to apply specific narrative concepts and methods with diverse clinical issues/problems; learning to collaborate with children and respect them as experts in their own lives; and shifting the nursing focus from a problem-focused orientation to a strength-oriented and child-centered approach.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".