An investigation of the factors relating to attendance of psychological appointments : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Clinical Psychology at Massey University, Auckland, New Zealand
Bibliographic record
Abstract
Background Psychological therapy is an important tool to improve mental health concerns. However, the high prevalence of mental health concerns is not reflected by mental health service use. Many individuals who are referred to a service do not attend or do not complete therapy. \nMethods A quantitative cross-sectional survey design was used to investigate psychological and practical factors which may impact attendance of psychological appointments. The factors investigated included: therapy anxiety, safety behaviours, intrinsic motivation, stigma, fear of disclosure, cultural safety, and practical factors. One qualitative method using an open ended question at the end of the survey was used to elicit further factors beyond the main survey questions that may predict non-attendance. Following exclusions, 669 participants were included in the final sample from Australia, New Zealand, Canada and the United Kingdom. \nResults The results of the study found statistically significant relationships between non-attendance and the following factors: therapy anxiety, safety behaviours, intrinsic motivation and self-stigma. Among the practical factors investigated, three of the 12 factors demonstrated statistically significant relationships with non-attendance these included, part-time employment, forgotten appointments, and family commitments. The results of the qualitative analysis highlighted five main categories of factors identified by participants. These categories included: psychological factors, practical factors, clinical factors, other commitments, and service factors. \nConclusions Of the factors investigated in this study, therapy anxiety was the strongest psychological predictor of not attending therapy across the statistical models. Furthermore, therapy anxiety was one of the most self-reported reasons for not attending psychological appointments. While therapy anxiety was the strongest predictor, the study demonstrated a range of factors which related to individuals’ likelihood of attending psychological appointments. The findings of the current study may suggest that interventions that target a range of the most commonly identified factors would be more effective than trying to target just one of the various factors that cause non-attendance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".