Should There Be a Cap on the Number of Patients Under the Care of a Clinical Pharmacist?
Bibliographic record
Abstract
PRO" SIDEIn the ideal health care system, there would be an abundance of resources to ensure timely and comprehensive patient care.However, it is a well-known reality that demands on Canadian hospitals and clinical pharmacy services are escalating because of increases in the number of elderly patients, the acuity of patients' conditions, the complexity of drug regimens, and the length of stay in hospital.In addition, there continues to be a shortage of hospital pharmacists.Despite these challenges, patient care should not be compromised.Hence, we believe that there should be a cap on the number of patients under the care of a clinical pharmacist.We outline here the 4 main reasons for this position.First, not limiting the number of patients under the care of a clinical pharmacist may compromise patient care and may actually increase costs.2][3] If individual pharmacists are each expected to take care of a large number of patients, it may not be possible for them to perform all of these mortality-reducing interventions for all assigned patients. 15urthermore, Bond and Raehl 3 have demonstrated an association between the number of pharmacists per 100 beds and mortality.In addition, when pressed for time, pharmacists may only deal with urgent issues or troubleshoot problems and may not consistently perform certain cost-saving activities such as development and management of drug protocols, making switches from IV to oral dosage forms, or changing therapy to less expensive alternatives.Second, a heavy patient load may be detrimental to the pharmacist's relationship with other health care professionals.If the pharmacist has an excess number of patients to see, he or she may be forced to provide targeted services to patients at various locations in an institution and may thus be unable to develop consistent relationships with the other health care professionals on the patient care team.The value of the pharmacist is realized when he or she practises in a collaborative, integrated environment and when other health care professionals can place a face to a name.Pharmacists are more likely to be consulted if they are present in person than if they have to be paged or called.Scaling back services may suggest to other health care professionals that pharmacists are only capable of targeted tasks,
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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 | 0.027 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.031 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".