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Record W2414859584 · doi:10.1037/fsh0000153

Mothers’ use of blogs while engaged in family-based treatment for a child’s eating disorder.

2015· article· en· W2414859584 on OpenAlexaff
Andrea LaMarre, Jane Robson, Anna Dawczyk

Bibliographic record

VenueFamilies Systems & Health · 2015
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsycINFOThematic analysisPsychologyContext (archaeology)Social mediaValue (mathematics)Developmental psychologySocial psychologyQualitative researchMEDLINEWorld Wide WebSociology

Abstract

fetched live from OpenAlex

INTRODUCTION: We explore parents' use of blogs while engaged in family-based treatment (FBT), a form of treatment in which parents engage as the primary givers of care for a child's eating disorder. We sought to bring together emergent literature on the value of blogging for social support with a body of literature on caregiving for a child with an eating disorder and to understand how parents use blogs while engaged in FBT. METHOD: We conducted a thematic analysis of 138 blog entries written by 5 mothers. RESULTS: Two main themes emerged: the importance of support and shifts in parenting. Blogs detailed how parents actively seek to meet their needs during a difficult time using online interactions to bolster sources of support that exist offline. This intensive form of treatment also provoked shifts in parenting, which parents described on their blogs. Parents' blogs were rich with descriptions of their use of mutually reinforcing on- and offline support. DISCUSSION: The unique context of the blogs allowed for access to data that were not generated for the purpose of research. Results add to the growing body of literature about parents' caregiving experiences and use of blogs for social support, and they offer implications for using online spaces as adjunct support for families. (PsycINFO Database Record

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.136
GPT teacher head0.354
Teacher spread0.218 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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