Knowledge and Attitude of Basic Psychiatric Trainees in Ireland to the Clinical Indemnity Scheme
Bibliographic record
Abstract
OBJECTIVES: To assess the knowledge of basic psychiatric trainees in Ireland of the Clinical Indemnity Scheme (CIS) and to ascertain how many basic specialist trainees in psychiatry had obtained additional medical indemnity cover and reasons for obtaining additional cover. METHOD: A structured questionnaire was distributed by post to 300 basic specialist trainees in psychiatric training schemes in Ireland. The questionnaire enquired about demographic details and examined the level of trainees' knowledge of the clinical indemnity scheme. Results were compiled and analysed using descriptive statistics and SPSS version 14. RESULTS: The response rate was 49%. The bulk of respondents were male (65.5%), aged between 30-35 years of age (44.6%). The majority of the respondents were aware of the CIS, with approximately half of the respondents having acquired additional medical indemnity cover. The level of awareness of the CIS was proportionately more amongst male respondents (69.1%), compared with females (58.5%). However, more females (61.5%) had additional medical indemnity cover compared with males (45.5%). Irish national trainees were more aware (72.9%) and had additional medical indemnity (80%), compared with non-Irish national trainees of whom approximately 61% were aware of the CIS and only 40% had an additional cover. The level of knowledge regarding details of what the CIS provided coverage for was quite poor. Respondents who had obtained additional indemnity were unsure what cover their additional indemnity provided. Only 10 respondents had been involved in medico-legal cases and of these, five had medical indemnity at the time of the case, stating that the legal advice and support was helpful. CONCLUSION: Our survey has highlighted that a considerable number of basic specialist trainees in psychiatry in Ireland had no detailed knowledge of what the CIS indemnifies them for and what situations were not covered by the scheme. Additionally, it revealed a clear split in favour of Irish national trainees in comparison to non-Irish national trainees in terms of awareness of the CIS and the procurement of additional medical indemnity. There needs to be an educational drive to provide more information to psychiatric trainees regarding the CIS and other medical insurance schemes. Furthermore, it would be important to examine factors that influence trainees in obtaining/not obtaining additional cover.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".