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Record W3005817529 · doi:10.1111/cch.12761

Categorizing mothers' and fathers' conceptualizations of children's serious play‐related injuries: “You won't grow a finger back”

2020· article· en· W3005817529 on OpenAlexafffundabout
Michelle E. E. Bauer, Mariana Brussoni, Audrey R. Giles

Bibliographic record

VenueChild Care Health and Development · 2020
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsBC Children's HospitalSpinal Cord Injury BCUniversity of British ColumbiaUniversity of Ottawa
FundersInstitute of Gender and HealthCanadian Institutes of Health Research
KeywordsDevelopmental psychologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: There is inconsistency across child development and care literature in operationalizing serious play-related injury and also a lack of understanding of how mothers and fathers conceptualize serious play-related injury. The current study explores parents' perspectives of their 2- to 7-year-old children's serious play-related injuries in urban and rural areas of British Columbia and Québec, Canada, and provides an urban/rural and gender analysis of the results. METHODS: We conducted semistructured interviews with 41 mothers and 63 fathers from 57 families, a total of 104 participants, in urban and rural locations in British Columbia and Québec, Canada. We used a social constructionist approach to the research and reflexive thematic analysis to construct themes from participant responses and to inform the consequent categorizations of serious play-related injury. RESULTS: The results indicate four categories of parents' conceptualizations of serious play-related injury: (a) injury requiring medical intervention, (b) injury resulting in head trauma, (c) injury resulting in debilitation, and (d) broken bones. CONCLUSIONS: Child development and care advocates can use these categories to strengthen their communications with parents and to improve understanding of parents' conceptualizations of children's serious play-related injury.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.280
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes3
Has abstractyes

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