Working with parents of children with complex mental health issues to improve care: A qualitative inquiry
Bibliographic record
Abstract
The study objective was to explore the experiences of parents of children (6–17 years) with complex mental healthcare needs in accessing healthcare services in Alberta, Canada. Parents were interviewed using a semi-structured guide with open-ended and probing questions. Interviews were audio recorded and transcribed verbatim. Thematic analysis revealed three main themes: (1) Fragmented healthcare services profoundly impacted participants’ experience of mental health care due to (a) a lack of a collaborative approach across disciplines in the healthcare system; (b) unavailability of information related to mental health care and (c) a lack of patient-centred care. (2) Navigating the complex healthcare system was difficult due to fragmented services and was hindered by gaps in accessing and receiving care, lack of continuity of care and lack of resources. (3) Distressed parents discussed the emotional challenges, financial burdens, self-advocacy and stigma they experienced in navigating the system. Parents offered insights into potential solutions to these gaps. Parents recommended the creation of a one-stop shop service with a team approach led by a navigator to facilitate and support navigations across healthcare services that work collaboratively across disciplines among healthcare services and across sectors inclusive of social services, education, policing and community programmes.
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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 | 0.012 | 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 | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".