A French Adaptation and Validation of Retirement Semantic Differential (RSD)
Bibliographic record
Abstract
Retirement is associated with numerous representations, some of them being negative and the other positive. Yet, these representations affect the health of individuals in their transition to retirement. However, although the socio-political context in France favors the emergence of numerous representations of retired people, to our knowledge there is no scale validated in French that would allow us to evaluate them. Thus, the objective of this study was to adapt and validate a scale assessing representations of retired people, called the Retirement Semantic Differential (RSD), for a French population. The scale consists of a series of bipolar adjectives related to retirement, such as "active/passive" and "happy/sad", with participants' responses indicating the connotative meaning, positive or negative, about representations of retirement. A total of 279 participants aged 18 to 55 years, recruited online, completed the adapted version of the RSD. The results show that the scale has good psychometric properties. The analysis found a three-factor model, and some items were removed, resulting in a reduced version of the scale (11 items). The results will be discussed in terms of cultural and socio-political differences. This scale could contribute to a better understanding of the deleterious effects on health of the transition to retirement and serve to improve the effectiveness of interventions aimed at reducing the negative effects of these representations upon young retirees or those preparing for retirement.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".