Social-ecological considerations informing a universal screening strategy for sleep health in the community
Bibliographic record
Abstract
“Poor sleep health” (PSH), defined as reduced amount of sleep and non-restorative sleep, affects cognitive, social and emotional development. Evidence suggests an association of sleep deprivation and mental health problems; however, there are no universal concepts allowing a first-tier screening of PSH at a community level. The focus of this narrative review is to highlight the cultural context of the current medicalized approach to PSH and to suggest social ecological strategies informing new and holistic community-based screening concepts. We present two conceptual screening frameworks; a “medical” and a merged “social emotional wellbeing framework” and combine them utilizing the concept of “ecologies.” The first framework proposes the incorporation of “sleep” in the interpretation of “vigilance” and “inappropriate” labeled behaviors. In the first framework, we provide a logic model for screening the myriad of presentations and possible root causes of sleep disturbances as a tool to assess daytime behaviors in context with PSH. In the second framework, we provide evidence that informs screening for “social emotional wellbeing” in the context of predictive factors, perpetuating factors and predispositions through different cultural perspectives. The distinct goals of both frameworks are to overcome training-biased unidirectional thinking anda priorimedicalization of challenging, disruptive and/or disobedient behaviors. The latter has been explicitly informed by the critical discourse on colonization and its consequences, spearheaded by First Nations. Our “transcultural, transdisciplinary and transdiagnostic screening framework” may serve as a starting point from which adaptations of medical models could be developed to suit the purposes of holistic screening, diagnosis, and treatment of complex childhood presentations in different cultural contexts.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".