Unpacking family meals: state-of-the-art review critiquing the Western ideals, positioning and promotion of family meals
Bibliographic record
Abstract
Family meals are a familiar concept and are practised in many Western households. While academics have been researching family meals for decades, there is much about the family meal we still do not understand. Meanwhile, the promotion of an 'ideal' of family meals across media, health and social discourse ensues. An extensive pool of research has indicated cross-sectional associations between the frequency of family meals and optimal health outcomes. However, evidence surrounding what specifically it is about the family meal that could offer health benefits is limited. Furthermore, family meals carry a level of moral value, evoking pressure for parents to 'achieve' a family meal ideal, unattainable for many. Family meals research has traditionally focussed on mothers' perceptions and roles in family mealtimes. While literature is starting to emerge on the often-overlooked cognitive contribution required to plan and execute family meals, little structural supports exist to streamline these efforts or make them more achievable for contemporary families. The published literature is only starting to include cultural and demographic diversity, making it difficult to understand and promote realistic and feasible family meals across populations. The popular depiction of family meals needs to evolve alongside changes in social norms. Our call to action to address these issues is to draw on existing family meal practices, continue to diversify our investigations, and broaden our definitions and understandings of what a family meal is or could be, and how it should be measured.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".