Treatment Planning to Support Adult Clients Experiencing Anticipatory Anxiety
Bibliographic record
Abstract
Anticipation anxiety (AA) is categorised as fear and uncertainty about an upcoming dreaded event. The author of this literature review evaluated key quantitative research findings from the last 10 years focused on adult populations that inform treatment interventions available for AA. Clients commonly seek treatment to decrease AA symptoms, reduce their fears and avoidance, cope with uncertainty, and gain the ability to engage in feared situations. Statistics Canada (2021) found that 15% of adult Canadians battle generalised anxiety disorder. Anxiety frequently presents in counselling, and maladaptive AA creates challenges for an even larger demographic because the vast contexts and factors impact its prevalence. The studies that the author accessed for this literature review reveal the AA predictors and critical factors that impact experience and maintain symptoms. Targeting these factors and treating them with evidence-based interventions will help clients to develop skills to reduce their distress. Cognitive behavioural therapy (CBT) has demonstrated the largest effect in reducing AA symptoms over other tested approaches; however, choosing appropriate treatment is not straightforward. The implication for clinical practice might be a more holistic appraisal considering the therapeutic alliance and treatment fit with clients' worldviews and therapeutic styles. The concept map (Figure 1) illustrates a path to evaluate and plan treatment that integrates these findings. Gaps in the understanding of clients’ lived experiences require an increase in qualitative research designs. Researchers who conduct future quantitative studies must expand the focus of research on AA by testing a larger sample size and treatment efficacy from theoretical orientations other than cognitive behavioural.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".