MétaCan
Menu
Back to cohort
Record W2017739749 · doi:10.1177/0008417413520489

Belonging, occupation, and human well-being: An exploration

2014· article· en· W2017739749 on OpenAlexvenueno aff
Karen R. Whalley Hammell

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessOccupational therapyContext (archaeology)SociologyPsychologySocial psychologyWell-beingPublic relationsPolitical sciencePsychotherapistGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Researchers identify the importance of belonging to human well-being and provide evidence-based support for occupation as a medium for expressing and achieving a sense of belonging and connectedness. PURPOSE: The purpose of this article is to highlight the imperative for occupational therapy theory and practice to address occupations concerned with belonging needs. KEY ISSUES: Dominant occupational therapy models emphasise doing self-care, productive, and leisure occupations, thereby ignoring occupations undertaken to contribute to the well-being of others, occupations that foster connections to nature and ancestors, collaborative occupations, and those valued for their social context and potential to strengthen social roles. IMPLICATIONS: Belonging, connectedness, and interdependence are positively correlated with human well-being, are prioritized by the majority of the world's people, and inform the meanings attributed to and derived from the occupations of culturally diverse people. If occupational therapy is to address meaningful occupations, attention should be paid to occupations concerned with belonging, connecting, and contributing to others.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.258
GPT teacher head0.502
Teacher spread0.245 · 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

Citations149
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Occupational TherapySame topicOccupational Therapy Practice and ResearchFrench-language works237,207