Psychosocial adaptation: an evolutionary concept analysis exploring a common multidisciplinary language
Bibliographic record
Abstract
AIMS: To provide the first known concept analysis of psychosocial adaptation, exploring its evolution from the concept adaptation. We also determine how psychosocial adaptation is conceptualized across nursing, health, sociobehavioural and education disciplines. BACKGROUND: Psychosocial adaptation is an important conceptual term that is poorly defined in nursing and other health, sociobehavioural and education disciplines. A thorough understanding of the concept's application in nursing and across disciplines can help to clarify its meaning, facilitate a more effective common language between disciplines and inform future psychosocial adaptation research. DESIGN: Rodger's evolutionary view guided this concept analysis. DATA SOURCES: Peer-reviewed English and Spanish manuscripts published between 2011-2013 were retrieved from the following databases: CINAHL, Psych INFO, PubMed, Scopus and LILACS. REVIEW METHODS: Eighty-nine articles related to psychosocial adaptation were included in the analysis. Findings identify key attributes, antecedents and consequences associated with the use of the concept. Findings were compared vis-a-vis reported characteristics of adaptation. RESULTS: The attributes characterizing psychosocial adaptation are: change, process, continuity, interaction and influence. In psychosocial adaptation, new life conditions serve as antecedents, while consequences are good or bad outcomes. Important features of the evolution of this concept include its broad appropriation across the reviewed disciplines. The attributes of psychosocial adaptation, have some similarities to those of general adaptation. Both concepts include an aspect of change, but unlike adaptation, psychosocial adaptation has branched away from biological descriptors, such as homeostasis and tends to focus on relational characteristics, such as interaction and influences.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| 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".