LANG AND GOULET HARDINESS SCALE: DEVELOPMENT AND TESTING ON BEREAVED PARENTS FOLLOWING THE DEATH OF THEIR FETUS/INFANT
Bibliographic record
Abstract
The process of development and testing of the Lang and Goulet Hardiness Scale (LGHS), a self-report instrument designed to measure hardiness in bereaved parents following the death of their fetus/infant, is presented. Hardiness is a personal resource, composed of 3 interdependent components that are characterized by a sense of personal control over the outcome of life events and hardships such as the death of a fetus/infant, an active orientation toward meeting the challenges brought on by the loss, and a belief in the ability to make sense of one's own existence following such a tragedy. The concept of hardiness has been studied by various disciplines and in a multitude of settings to understand its ability to lessen potentially negative effects of life stress. However, it has never been studied within the context of parental bereavement. The LGHS was developed systematically, originating from a concept analysis. A panel of 15 experts was used to establish content validity.A pretest was conducted on 73 bereaved individuals to assess convergent and discriminant validity of the LGHS. Subsequently, a validation study on 220 bereaved parents who had experienced the death of their fetus/infant 2 months previously was conducted including a retest 6 months after the loss with 192 of the remaining participants. Analyses reveal that the LGHS is a valid and reliable instrument for measuring hardiness and that it is sensitive enough to detect changes in the construct over time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".