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Record W3110731023 · doi:10.1080/1364436x.2020.1856048

Developing a definition of spiritual health for Canadian young people: a qualitative study

2020· article· en· W3110731023 on OpenAlexafffundabout
Valerie Michaelson

Bibliographic record

VenueInternational Journal of Children s Spirituality · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsBrock University
FundersCanadian Institutes of Health Research
KeywordsOperationalizationQualitative researchHealth promotionSpiritual HealthAsset (computer security)Value (mathematics)Focus groupPsychologyHealth policyPublic relationsSociologyMedicineSocial psychologyNursingPolitical sciencePublic healthEpistemologySocial scienceClinical psychology

Abstract

fetched live from OpenAlex

While the spiritual dimensions of health are often tangentially recognized in the health sciences, minimal direction is given as to what spiritual health is, or to what it means to policy or practice. In Canada, this lack of understanding is problematic because despite strong evidence suggesting that spiritual health can operate as a protective health asset in the lives of young people, it is difficult to create effective health promotion strategies for supporting spiritual health without clear definitional agreement. Guided by interpretive description as a methodological orientation, I conducted a qualitative study (n=74) with the goal of developing a definition of spiritual health that would have practical value for Canadian young people and could be used to support the optimization of their health. Data were generated through focus groups and interviews. Results yielded a child-informed definition that provides a clear starting place for operationalizing spiritual health in health-related contexts.

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)0.013
metaresearch head score (Gemma)0.014
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 score0.085
Threshold uncertainty score0.565

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0280.013
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0020.004
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 head0.104
GPT teacher head0.437
Teacher spread0.334 · 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

Citations15
Published2020
Admission routes3
Has abstractyes

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