A concept analysis of children with complex health conditions: implications for research and practice
Bibliographic record
Abstract
Abstract BACKGROUND: This concept analysis aimed to clarify the meaning of “children with complex health conditions” and endorse a definition to inform future research, policy, and practice. METHODS: Using Walker and Avant’s (2011)’s approach, we refined the search strategy with input from our team, including family representatives. We reviewed the published and grey literature. We also interviewed 84 health, social, and educational stakeholders involved in the care of children with complex health conditions about their use/understanding of the concept. RESULTS: We provided model, borderline, related, and contrary cases for clarification purposes. We identified defining attributes that nuance the concept: (1) conditions and needs’ breadth; (2) uniqueness of each child/condition; (3) varying extent of severity over time; 4) developmental age; and (5) uniqueness of each family/context. Antecedents were chronic physical, mental, developmental, and/or behavioural condition(s). There were individual, family, and system consequences, including fragmented services. CONCLUSIONS: Building on previous definitions, we proposed an iteration that acknowledges the conditions’ changing trajectories as involving one or more chronic condition(s), regardless of type(s), whose trajectories can change over time, requiring services across sectors/settings, oftentimes resulting in a lower quality of life. A strength of this paper is the integration of the stakeholders’/family’s voices into the development of the definition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.155 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".