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Record W2022101821 · doi:10.1177/1074840714532716

Understanding Parental Experiences Through Their Narratives of Restitution, Chaos, and Quest

2014· article· en· W2022101821 on OpenAlexaff
Jill Bally, Lorraine Holtslander, Wendy Duggleby, Karen Wright, Roanne Thomas, Shelley Spurr, Christopher Mpofu

Bibliographic record

VenueJournal of Family Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of OttawaSaskatchewan Cancer AgencyUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsNarrativeDialogical selfRestitutionNarrative inquiryPsychologyIsolation (microbiology)Developmental psychologyLived experienceSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

The purpose of this secondary analysis was to develop an enhanced understanding of the experiences of parents who have children in treatment for cancer. Data collected from 16 parents (12 mothers and 4 fathers) were analyzed using Frank's dialogical narrative analysis. Findings demonstrated that parents' experiences were represented in chaos, restitution, and quest narratives. Each of these narratives was only one instance of a very complex and changing parental experience that cannot be understood in isolation from the others. The holistic understanding provided by these findings contributes to a more comprehensive understanding of parental experiences of their child's illness and highlights the need for health professionals to invite conversations about parents' illness experience and attend to the specific narrative type parents are presenting to support them adequately. Additional research is required to develop supportive approaches for each narrative which takes into account the complexities of parents' experiences.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
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 head0.157
GPT teacher head0.373
Teacher spread0.216 · 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

Citations41
Published2014
Admission routes1
Has abstractyes

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