Stimulating Innovations in the Measurement of Parenting Constructs
Bibliographic record
Abstract
Parents can play a crucial role in the development of children's behaviors associated with dietary habits, physical activity, and sedentary lifestyles. Many parenting practices and/or styles measures have been developed; however, there is little agreement as to how the influence of parenting should be measured. More importantly, our ability to relate parenting practices and/or styles to children's behaviors depends on its accurate assessment. While there is a need to standardize our assessment to further advance knowledge in this area, this article will discuss areas that may stimulate advances in the measurement of parenting constructs. Because self-report measures are important for the assessment of parenting, this article discusses whether solutions to improve self-report measures may lie in: (1) Improving the questions asked; (2) improving the methods used to correct for social desirability or measurement errors; (3) changing our measurement paradigm to assess implicit parenting behaviors; (4) changing how self-report is collected by taking advantage of ecological momentary assessment methods; (5) using better psychometric methods to validate parenting measures or alternatively using advances in psychometric methods, such as item banking and computerized adaptive testing, to solve common administration issues (i.e., response burden and comparability of results across studies); and (6) employing novel technologies to collect data such as portable technologies, gaming, and virtual reality simulation. This article will briefly discuss the potential of technologies to measure parenting constructs.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".