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Record W1977055423 · doi:10.1177/1715163513507535

Patient-centred care

2013· article· fr· W1977055423 on OpenAlexvenueno aff
Lisa Zaretzky

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2013
Typearticle
Languagefr
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsNursingPsychologyMedicine

Abstract

fetched live from OpenAlex

Thank you for your recent editorial1 with its entertaining and informative example of patient-centred care (PCC). (Since I already serve ethically sourced coffee and have lovely plants, the only thing lacking in my practice seems to be a leather sofa. . . .) I do position myself as a PCC provider, so I started to think of my own patient interactions that could exemplify “a professional obligation to take responsibility for, and provide care targeted to, the individual patient’s needs.”1 Like most community pharmacies, I provide weekly pill pack medications that aim to increase ease of adherence to the chosen drug therapy. But what if the patient is consistently not able to access their pill packs, resulting in (undisclosed) undertreatment? CH, a 47-year-old woman with fibromyalgia, hypertension, hypercholesterolemia, depression and restless leg syndrome, came into the pharmacy with her 12-year-old daughter to pick up her 4-week pill packs. Previously, we had provided CH with a separate bedtime pill pack, since her bedroom was on the second storey of their home and her pill pack was usually left in the kitchen (to facilitate better adherence of other medications). CH reported the separate bedtime pill pack to be extremely useful. Upon reviewing the contents of her current pill pack, CH’s daughter disclosed that her mother’s supper pills were often “left over.” CH explained that it was difficult to remember to take the supper pills and that they were often skipped. The medications that were intended for supper dosing were as follows: Ramipril 10 mg (1 of 2 daily doses) Nifedipine XL 20 mg (entire daily dose) Rabeprazole 20 mg (1 of 2 daily doses) Pregabalin 50 mg (1 of 3 daily doses) Undertreatment of her hypertension was my first concern. Although CH did not have time for me to take her blood pressure, I did request that we check it at her next visit. She did admit to not feeling the “greatest” and sleeping poorly. I also inquired about her previous stomach complaints. CH reported that the rabeprazole was to treat belching following eating meat, which had neither gotten worse or better since the physician initiated the treatment and even increased the dose from every day to twice a day, although many of the second doses were admittedly missed. I reviewed the supper dosing with CH and asked if she would be agreeable to switching the medications over to bedtime if it was reasonable to do so. CH agreed, and I called the physician to discuss CH’s medication management. According to the physician, CH’s blood pressure (as per clinic visits) had not been well controlled, so the physician agreed to institute my changes before increasing doses or adding any new medications. CH’s objective and subjective symptoms would be reviewed at the next clinic visit, as well as at the next pharmacy visit. The following were the changes made for CH: Ramipril 10 mg AM and supper changed to AM and before bedtime Nifedipine XL 20 mg at supper changed to before bedtime Rabeprazole 20 mg AM and supper changed to once a day, since it had not been any more effective for her “burping” after meat consumption with the twice-a-day dosing Pregabalin 50 mg AM, supper and before bedtime changed to 50 mg AM and 100 mg before bedtime Follow-up with CH on adherence and disease management is still pending. I feel that this scenario is a good example of listening to the patient’s needs and creating medication management techniques that are better suited to the individual while still optimizing pharmacological effectiveness. There are more glamorous examples of patient-centred care, but this is a simple example of potentially preventing unnecessary increases in medication therapy. PS: I also really appreciated the literature review on the top 5 cardiology studies from 2011 to 2012. I do hope you provide us with more! ■

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.201
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0040.010
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.2010.047

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.

Opus teacher head0.240
GPT teacher head0.418
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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