Narratives of transformation and recovery in New Zealand Defence Force personnel accessing mental health support
Bibliographic record
Abstract
Introduction: Little is known about user experiences of accessing mental health care among active duty New Zealand Defence Force (NZDF) personnel. Although research exists on barriers to care and treatment outcomes, there is a dearth of understanding of the experiences between these two areas of focus. This study describes the narratives of 21 active duty personnel who sought mental health support in the NZDF health care system. Methods: Participant accounts were generated through semi-structured interviews and analysed using a narrative approach. The focus of analysis was to provide a deeper understanding of how participants storied their experiences from the onset of mental distress to recovery, with a focus on the narrative structure of these experiences. Results: Participant narratives often followed the progression of a quest narrative, mirroring the hero's journey, which encompassed background factors leading to distress, chaos and breakdown, serendipitous connection to support, recovery, and redemption. Engaging with the health care system marked the beginning of personal transformation and posttraumatic growth. Transformation involved changing core beliefs and perspectives, leading to increased self-awareness and empathy and ultimately contributing to growth as individuals and leaders within the NZDF. Discussion: This study underscores the importance of understanding and facilitating posttraumatic growth in military personnel in both clinical and organizational contexts. It highlights the need for mental health messaging and support within military institutions to align with existing cultural values and narratives. Overall, the study contributes to a deeper understanding of the complex journey of recovery and growth in military mental health care contexts.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".