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Record W4239059638 · doi:10.22215/etd/2013-09993

A Qualitative Study of Fathers' Experiences of Depression After Having or Adopting a Child

2013· dissertation· en· W4239059638 on OpenAlexaff
Lilly Pease

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyThematic analysisStressorDepression (economics)Developmental psychologyQualitative researchClinical psychologySociologySocial science

Abstract

fetched live from OpenAlex

To gain insight into the lived experiences of paternal postpartum and post-adoption depression, semi-structured interviews lasting an average of 73 minutes were conducted with 4 biological and 3 adoptive fathers who self-identified as primary caregivers and experienced depression within 12 months of having or adopting their child(ren).A thematic analysis revealed commonalities and unique aspects of biological and adoptive fathers' experiences of depression, as well as society's influence on their experiences of depression.Commonalities included the impact of Significant Stressors and Expectation-Reality Asymmetry on fathers' experiences of depression, while biological fathers had difficulty being Mentally Present with their Children, and adoptive fathers had a Strong Need to Teach their Children.Fathers also described how aspects of society such as Social Expectations of Parents and their Unmet Support Needs perpetuated their depression.These findings are discussed in relation to research regarding mothers' experiences of PPD, and their implications for practice and research are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.400
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2013
Admission routes1
Has abstractyes

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