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Record W4402374069 · doi:10.4081/qrmh.2024.12318

Does the decision to disclose non-suicidal self-injury align with decision-making frameworks of personal information disclosure? A directed content analysis

2024· review· en· W4402374069 on OpenAlexaff
Sylvanna Mirichlis, Penelope Hasking, Mark Boyes, Stephen P. Lewis, Kassandra Hon

Bibliographic record

VenueQualitative Research in Medicine & Healthcare · 2024
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
FundersMedical Research CouncilNational Health and Medical Research CouncilCurtin University of Technology
KeywordsCoding (social sciences)PsychologyContent analysisSelf-disclosureFull disclosureHuman factors and ergonomicsApplied psychologyPoison controlSocial psychologyMedicineComputer scienceMedical emergencyComputer security

Abstract

fetched live from OpenAlex

= 1.88), with 11 identifying as female. All participants had lived experience of NSSI which they had previously disclosed to at least one other person. All codes within the coding matrix, which were informed by the disclosure models, were identified as being present in the data. Of the 229 units of data, 95.63% were captured in the existing frameworks with only 10 instances being unique to NSSI disclosure. Though factors that inform the decision to disclose NSSI largely align with the aforementioned models of disclosure, there are aspects of disclosure decision-making that may be specific to NSSI.

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)0.016
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0040.011
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.232
GPT teacher head0.593
Teacher spread0.361 · 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.

Study designQualitative
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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