The Effects of Financial Record Keeping on Financial Performance of Development Groups in Rural Areas of Western Uganda
Bibliographic record
Abstract
Good financial recordkeeping enables business organizations to plan properly and also check for misappropriations of resources. This manuscript expounds a study on financial record keeping as an integral part of managing development groups’ business environment. The study was carried out in three rural districts of Rubirizi, Kasese and Rukungiri in Western Uganda with the aim of determining the effects of financial record keeping on financial performance of Development groups. A total of 99 respondents were obtained from 33 development groups. In every development group, three leaders i.e. the Chairperson, Treasurer and Secretary were interviewed. Data were entered in Epi data 3.1 and descriptive and correlational analyses were done using SPSS version 21. Using the Likert scale with five categories from strongly agree to strongly disagree, a mean value of 3.5 indicated a strong agreement to the questions asked. The mean value (4.32) on access to information implied that when members have access to information; transparency and accountability are ensured. Findings indicated that 67 (67.7%) knew the type of financial books recognized by international accounting standards board (IASB). 36 (36.4%) respondents reported keeping money in boxes while 22 (22.2%) kept money in the bank. Findings revealed a significant positive relationship between the financial record keeping and financial performance (r=0.297**, P>0.05). Notwithstanding the above findings, there is need to train group leaders in financial recordkeeping.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".