Selective Attentional Processing to Fall-Relevant Stimuli Among Older Adults Who Fear Falling
Bibliographic record
Abstract
Fear of falling is known to affect more than half of community-dwelling older adults over 60 years of age. This fear is associated with physical and psychological effects that increase the risk of falling. The authors' theory is that attentional processing biases may exist in this population that serve to perpetuate fear of falling and subsequently increase fall risk. As a starting point in testing this proposition, the authors examined selective attentional processing bias to fall-relevant stimuli among older adults. Thirty older adult participants (M(age) = 70.8 ± 5.8), self-categorized to be Fearful of Falling (FF, n = 15) or Non-Fearful of Falling (NF, n = 15) completed a visual dot-probe paradigm to determine detection latencies to fall-threatening and general-threat stimuli. Attentional processing was defined using three index scores: attentional bias, congruency index, and incongruency index. Bias indicates capture of attention, whereas congruency and incongruency imply vigilance and disengagement difficulty, respectively. Both groups showed an attentional bias to fall-threat words but those who were fearful of falling also showed an incongruency effect for fall-threat words. These findings confirm that selective attentional processing profiles for fall-relevant stimuli differ between older adults who exhibit fear of falling and those who do not have this fear. Moreover, in accordance with current interpretations of selective attentional processing, the incongruency effect noted among fall-fearful older adults presents a possibility for a difficulty disengaging from fall-threatening stimuli.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".