Bibliographic record
Abstract
AIM: To report a simultaneous and evolutionary analysis of the concepts of joy and long-term happiness. BACKGROUND: Joy and happiness are underrepresented in the nursing literature, though negative concepts are well represented. When mentioned in the literature, neither joy nor happiness is adequately defined, explained, or clearly understood. To promote further investigation of these concepts in nursing and to explore their relationship with health and healing, conceptual clarity is an essential first step. DESIGN: Concept analysis. DATA SOURCES: The following databases were searched, without time restrictions, for articles in English: Academic Search Complete, Anthropology Plus; ATLA Religious Database with ATLASerials; Cumulative Index of Nursing and Allied Health Literature (CINAHL); Education Research Complete; Humanities International Complete; Psych EXTRA; and SocINDEX with Full Text. The final sample size consists of 61 articles and one book, published between 1978-2014. METHOD: An adapted combination of Rodgers' Evolutionary Model and Haase et al.'s Simultaneous Concept Analysis (SCA) method. RESULTS: Though both are positive concepts, joy and happiness have significant differences. Attributes of joy describe a spontaneous, sudden and transient concept associated with connection, awareness, and freedom. Attributes of happiness describe a pursued, long-lasting, stable mental state associated with virtue and self-control. CONCLUSION: Further exploration of joy and happiness is necessary to ascertain their relationship with health and their value to nursing practice and theory development. Nurses are encouraged to consider the value of positive concepts to all areas of nursing.
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 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".