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Record W2229914208 · doi:10.3928/0279-3695-20030301-11

Boosting Strategies: Enhancing the Self-Esteem of Individuals With Catastrophic Illnesses and Injuries

2003· article· en· W2229914208 on OpenAlexaff
Anne Dewar

Bibliographic record

VenueJournal of Psychosocial Nursing and Mental Health Services · 2003
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBoosting (machine learning)Grounded theoryPsychologyQualitative researchSocial psychologyMedicineSociologySocial science

Abstract

fetched live from OpenAlex

This qualitative study used grounded theory methods to determine how individuals were able to live with catastrophic illnesses and injuries. Twenty-eight participants were interviewed for stories of how they endured their circumstances. Although several strategies were used, this article describes a strategy entitled "boosting," which outlines the affected individuals' efforts to improve their self-esteem, which helped them bear their circumstances. Boosting has some relationship to social comparison theory. Implications for nurses and the importance of awareness of this communication need for patients 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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.007
GPT teacher head0.317
Teacher spread0.310 · 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 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

Citations6
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychosocial Nursing and Mental Health ServicesSame topicOptimism, Hope, and Well-beingFrench-language works237,207