Engagement complications of adolescents with borderline personality disorder: navigating through a zone of turbulence
Bibliographic record
Abstract
OBJECTIVE: Premature treatment discontinuation is a widespread phenomenon in child and adolescent mental health services that impacts treatment benefits and costs of care. Adolescents with borderline personality disorder (BPD) are heavy users of health care services and notoriously difficult to engage in treatment. However, there is hardly any data regarding this phenomenon with these youths. Considering that BPD treatment is associated with intense and chaotic therapeutic processes, exploring barriers emerging in the course of treatment could be relevant. Thus, conceptualizing treatment dropout as a process evolving from engagement to progressive disengagement, and ultimately to dropout, could highlight the mechanisms involved. The aim of this study was to describe the process of treatment disengagement and identify warning signs that foreshadow dropouts of adolescents with BPD. METHOD: A constructivist grounded theory method was used. This method has been favoured based on the assumption that the behaviours and decisions leading to disengagement may be better informed by the subjective experience of treatment. Thirty-three interviews were conducted to document 11 treatment trajectories with 3 groups of informants (9 adolescents with BPD 13-17 of age, 11 parents, and 13 clinicians). RESULTS: Well before dropout occurs, different phenomena identified as "engagement complications" characterize the disengagement process. These unfold according to a three-step sequence starting with negative emotions associated with the appropriateness of treatment, the therapeutic relationship or the vicissitudes of treatment. These emotions will then generate treatment interfering attitudes that eventually evolve into openly disengaged behaviours. These complications, which may sometimes go unnoticed, punctuate the progression from treatment engagement to disengagement leading the way towards the development of a "zone of turbulence" which creates a vulnerable and unstable therapeutic process presenting risk for late dropout. CONCLUSION: Engagement of adolescents with BPD is neither static nor certain, but on the contrary, subject to their fluctuating perceptions. Therefore, it can never be taken for granted. Clinicians must constantly pay attention to emergent signs of engagement complications. Maintaining the engagement of adolescents with BPD should be a therapeutic objective akin to reducing symptomatology or improving psychosocial functioning, and should therefore be given the same attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".