Occupational Engagement and Meaning: The Experience of Ikebana Practice
Bibliographic record
Abstract
Engagement and meaning are essential properties of occupation that have been associated with well-being in the occupational science literature. A deeper examination of subjective experiences while engaging in particular occupations may illuminate how specific occupational characteristics contribute to the relationship between occupation and well-being. Consequently, this study investigates the concepts of personal meaning and occupational engagement by exploring the experience of ikebana (Japanese flower arrangement) among practitioners. As a symbolic and disciplined art form anchored in cultural history, ikebana is a highly suitable occupation for examining subjective experiences of occupation and characteristics like personal meaning and engagement. Descriptive phenomenology informed the study design and data were gathered using semi-structured interviews with nine ikebana practitioners, including novices and teachers. Three themes identified through inductive analysis were: 1) ikebana as an avenue to a richer life; 2) transformation of the self through ikebana, and 3) ikebana supports harmony in life. The themes represent qualities of ikebana practice that facilitate ongoing engagement in this and other occupations, and support practitioners' sense of well-being. These findings reinforce the powerful influence of occupation on individual experience and elucidate the reciprocal relationships among meaning, engagement and well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".