“We’re in Good Hands There.” - Acceptance, Barriers and Facilitators of a Primary Care-based Health Coaching Programme for Children With Mental Health Problems: A Qualitative Study (Prima-quo)
Bibliographic record
Abstract
Abstract Background:About 17 % of children aged 3-17 years are affected by a mental health problem (MHP). Office-based paediatricians are the first in line to be contacted. Nevertheless, even for less severely affected patients, referral rates to specialised care are constantly high. A major statutory health insurance fund introduced a Health Coaching (HC) programme, including a training concept for paediatricians, standardized guidelines for actions, and additional payments to strengthen primary care consultation for MHP and to decrease referrals to specialized care. The aim of this study was to examine how the HC is perceived and implemented in daily practice to indicate potential strengths and challenges.Methods:In 2017 and 2018, a series of guideline-based interviews were conducted by phone with HC-developers, HC-qualified paediatricians, parents and patients (≥14 years) treated according to the HC programme. Paediatricians were selected from a Bavarian practice network. Parents of patients with the four most common MHP diagnoses were approached by their health insurance. All interviews were recorded and transcribed verbatim. Structuring content analysis derived from Mayring was used for analysis. Sample size was determined by saturation.Results:11 paediatricians, 3 developers, 22 parents and four adolescents were included. Families were generally satisfied with paediatric care received in the programme’s context. The HC supported paediatricians’ essential role as consultants and improved their diagnostic skills. Time and financial restrictions as well as patients’ challenging family structures were reported as major barriers to success.Conclusion: The HC programme is perceived as a facilitator for more patient-centeredcare, however, structural barriers remain.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".