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Record W2803236116 · doi:10.11124/jbisrir-2016-003123

The influence of occupation on wellbeing, as experienced by the elderly: a systematic review

2018· review· en· W2803236116 on OpenAlexaboutno aff
Christina Jessen-Winge, Mille Nyboe Petersen, Anne-Le Morville

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2018
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyPsychologySystematic reviewMedicineMEDLINEPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this review was to synthesize knowledge regarding which types of occupations the elderly perceive as important to and supporting their overall wellbeing. INTRODUCTION: Being active is an important part of managing the daily challenges of advanced old age. Occupational therapy and occupational science are based on the premise that a person's occupations are intertwined with their health and are thus needed for them to experience a meaningful life and improve their wellbeing. INCLUSION CRITERIA: This review, which included independently living elderly people aged 65 and over living in Europe, the USA, Canada and Australia, sought to uncover if the elderly see any specific types of occupations as enhancing their wellbeing. This review included qualitative studies with designs including, but not limited to, phenomenology, grounded theory, ethnography and action research. METHODS: A three-step search strategy using Joanna Briggs Institute methodology was utilized. Both published and unpublished studies were searched. The following databases were searched for studies up to 2017: CINAHL, Nursing and Allied Health Source, Web of Science, PubMed and OpenGrey. Using the JBI Critical Appraisal Checklist for Qualitative Research, two independent reviewers assessed whether each study had the methodological quality required to be included in the review. Any disagreements were resolved through discussion with a third reviewer. The standardized JBI data extraction tool for interpretive and critical research was used to extract data from the included studies. JBI tools were used to pool the qualitative research findings. The findings were then aggregated through a process of rating according to quality, and categorized according to similarity in meaning, which generated a set of statements for each category. These categories were then subjected to a meta-synthesis to produce a single comprehensive set of synthesized findings useful as a basis for evidence-based practice. RESULTS: All three of the included studies utilized qualitative interviews and followed a phenomenological approach. The studies included only participants aged 85 and over. Thirty-five findings were extracted and grouped into four categories, which were synthesized into two meta-syntheses: 1) The importance of "doing" alone - to feel good, I have to be the master of my own life; and 2) Old habits never die - a structured day is a good day. Based on the dependability and credibility of the three studies, the ranking was high (see Summary of Findings). CONCLUSIONS: The present review shows that two factors enhance elderly people's wellbeing: first, variation and independence in undertaking activities; second, having a choice between the occupations and a structure of activities that make up daily life. The two factors are influenced by a balance between having activities alone and with others. This review is not intended to identify specific occupations that enhance elderly people's wellbeing, but rather to indicate which types of occupations enhance the wellbeing of the elderly, and provide value and meaning to their daily lives.Both the dependability and the credibility of the studies have been rated as moderate.

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)0.012
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0130.013
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.159
GPT teacher head0.558
Teacher spread0.398 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations20
Published2018
Admission routes1
Has abstractyes

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