An interpretive phenomenological study of recovering from mental illness: Teenage girls' portrayals of resilience
Bibliographic record
Abstract
In this interpretive phenomenological study, understandings of resilience from the perspective of teenage girls recovering from mental illness were explored. The primary research question was as follows: How is resilience portrayed through teen girls' experiences of health and mental illness? Benner's interpretive phenomenology informed by Gadamerian concepts of conversation, prejudices, and fusion of horizons guided the research design. The interpretive description process involved close reading of how the world experienced by participants was understood while listening for relational, gendered and cultural nuances. Resilience for teenage girls recovering from mental illness was portrayed as very challenging yet a rewarding life path with complex lines of movement towards strength and wellness. The life path involved critical junctures and personal growth while withstanding multiple traumas and acting to overcome adversities. Findings from this study underscore for nurses the relevance of actively advocating for trauma-informed and gender-sensitive approaches to promote teenage girls' (mental) health and well-being and to enhance resilience and recovery from mental illness in the face of what may seem insurmountable adversities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".