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Record W4206390607 · doi:10.1007/978-3-030-79515-3_25

The Application of Salutogenesis for Social Support and Participation: Toward Fostering Active and Engaged Aging at Home

2022· book-chapter· en· W4206390607 on OpenAlexaff
Mélanie Levasseur, Daniel Naud

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsSalutogenesisIntervention (counseling)PsychologySocial supportGerontologyAging in placeSuccessful agingSocial psychologyHealth promotionMedicineNursingPublic health

Abstract

fetched live from OpenAlex

Abstract In this chapter, the authors discuss some important aging factors that could increase the likelihood of a stronger sense of coherence (SOC): aging at home, participation, and social support. In his last paper, Aaron Antonovsky (1993) highlighted an example of an intervention among older people, living in their homes, who refused to accept help. He suggested that if researchers had been guided by the salutogenic question of “how to strengthen the comprehensibility, manageability, and meaningfulness of elders,” their intervention research could have been much more sophisticated and rich. The authors are addressing this call. In this chapter, they analyze how social support, active participation, mobility, and other factors can strengthen SOC in old age. They also bring some examples of individual and community programs that are already operating within this salutogenic orientation.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.187
GPT teacher head0.460
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations7
Published2022
Admission routes1
Has abstractyes

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