Treatment planning: A key milestone to prevent treatment dropout in adolescents with borderline personality disorder
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to gain a broader appreciation of processes involved in treatment dropout in adolescents with borderline personality disorder (BPD). DESIGN: A constructivist grounded theory was chosen using a multiple-case research design with three embedded levels of analysis (adolescent, parent, and care setting). METHOD: Theoretical sampling and the different stages of analysis specific to grounded theory were performed according to the iterative process of constant comparative analysis. Twelve cases were examined (nine dropouts among adolescents with BPD and for the purpose of falsification, one dropout of suicidal adolescent without BPD and two completed treatments among adolescents with BPD). To document the cases, three groups of informants were recruited (adolescents, parents, and therapists involved in the treatment) and 34 interviews were conducted. RESULTS: Psychological characteristics, perception of mental illness and mental health care, and help-seeking context were the specific treatment dropout vulnerabilities identified in adolescents with BPD and in their parents. However, their disengagement became an issue only when care-setting response--including mitigation of accessibility problems, adaptation of services to needs of adolescents with BPD, preparation for treatment, and concern for clinicians' disposition to treat--was ill-suited to these treatment dropout vulnerabilities. Treatment planning proves to be a key milestone to properly engage adolescents with BPD and their parent. CONCLUSIONS: Systematic assessment of treatment dropout vulnerabilities before the intervention plan is laid out could foster better-suited responses of the care setting thus decreasing the incidence of treatment discontinuation in adolescents with BPD. PRACTITIONER POINTS: Treatment dropout vulnerabilities specific to adolescents with BPD and their parents can be detected before the beginning of treatment. Premature treatment termination may be prevented if the care setting considers these vulnerabilities at treatment planning. Fears and negative reaction to treatment can be attenuated if the reasons behind the therapeutic orientation are explained and if the adolescent understand how the treatment will affect his condition. Concerns for autonomy of the adolescent, parental help-seeking context as well as ensuring that the clinician has the skills, interest, and motivation to work with the family may have a positive effect on families' commitment to treatment.
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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 | 0.003 | 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) | 0.000 | 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".