Missed appointments in mental health care clinics: A retrospective study of patients’ profile
Bibliographic record
Abstract
Background: Missed appointments (no-shows) are a problem and common in outpatient clinics especially in psychiatric setting.Objective: This study aimed to describe the extent of no-shows in a regular psychiatric outpatient clinic, and to assess associations of missed appointments with patients’ demographic and clinical characteristics and types of services provided.Methods: Data collection from a hospital psychiatric clinic charts was conducted from administrative years 2017-18 and 2018-19, using descriptive analyses.Results: In the administrative year of 2017-18, the no-show rate was 9.5%, adding 10.7% for cancellations, for a total of 20.2%. In 2016-17, rates were 9.7%, with 17.3% cancellations, for a total of 27%. Rates varied from clinical groups (2.5% for borderline personality disorders patients to 30% for young psychotic patients) and by professionals (psychiatrists 5.6%, psychotherapists 23.3%) and for crisis services 21.9%.Conclusions: No-show numbers are comparable to other clinical sites but remain a challenge in delivering seamless and efficient services. A qualitative study will be conducted as a second phase to examine root causes and provide opportunities for service improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".